Eindhoven University of Technology MASTER On the road ... · Preface Eindhoven, 16th of May 2019 This thesis is the result of a six-month graduation project for the Master Operations

Eindhoven University of Technology

MASTER

On the road towards smart manufacturinga framework to support the development of smart manufacturing

Brouns, N.

Award date:2019

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On the road towardsSmart Manufacturing

A Framework to support the development ofSmart Manufacturing

Nadja Brouns

Department of Industrial Engineering and Innovation SciencesInformation Systems Research Group

Supervisors:

Prof. dr. ir. P.W.P.J. (Paul) Grefen, TU/e, IS

Dr. ir. I.T.P. (Irene) Vanderfeesten, TU/e, IS

Dr. O. (Oktay) Turetken, TU/e, IS

Drs. ing. R.J (Ronald) van Mersbergen, Atos

Ir. J.L.M. (Hans) Kwaspen, Atos

In partial fulfillment of the requirements for the degree of

Master of Sciencein Operations Management and Logistics

Eindhoven, May 2019

Eindhoven University of TechnologySchool of Industrial EngineeringSeries Master Theses Operations Management and Logistics

Keywords: fourth industrial revolution, smart manufacturing, reference architecture, framework, inform-ation systems, manufacturing industry, maturity model

ii On the road towards Smart Manufacturing

Abstract

The fourth industrial revolution is leaving its mark on society, influencing technology, business, and life-style. Similarly, to the three previous revolutions, it is expected to drastically change the manufacturingdomain. The change in the manufacturing domain is also known as the smart manufacturing initiat-ive. Companies that want to join and stay competitive need to adapt their operations by incorporatingInformation Technology and include the characteristics of smart manufacturing. These characteristicsinclude horizontal, vertical and end-to-end integration supported by Information Technology. In thisresearch, a framework is established that supports enterprises in pursuing smart manufacturing capabil-ities, based on their changing IT architectures. To extract the relevant elements of smart manufacturing,existing reference architectures are analyzed. Based on the outcome the elements that need to be in-cluded in the framework are known. These elements are structured in a framework according to variousstages, that visualizes a roadmap for smart manufacturing functionalities. The resulting framework isimplemented and the performance is evaluated.

On the road towards Smart Manufacturing iii

Preface

Eindhoven, 16th of May 2019

This thesis is the result of a six-month graduation project for the Master Operations Management andLogistics at Eindhoven University of Technology (TU/e), performed at Atos. This report also marksthe end of my six years as a student. I would like to take this opportunity to thank the people whosupported me along this journey.

First, I would like to thank my mentor Paul Grefen. Paul, thank you for your support in the past twoyears. Even though you were on a well-deserved sabbatical leave you still made time to discuss myproject and I especially enjoyed our digital meetings, when one of us was across the ocean. You gave meunique opportunities that were extremely valuable for my personal growth and truly enriched my Masterprogram. Second, I want to thank Irene Vanderfeesten as my second supervisor of this project. Theinput you delivered at the beginning of my thesis to direct me in the right direction and your constructivefeedback throughout the process was of great value. I also would like to thank Jonnro Erasmus, youmight not be officially my supervisor, but through our discussions on the actual design of the framework,I was able to gain a lot from your experience in the manufacturing domain.

Next, I want to thank Atos and especially my supervisors, Hans Kwaspen en Ronald van Mersbergen,for giving me the opportunity and tools to conduct this research. From the beginning, you gave me alot of freedom to choose a topic that suited me personally. This was challenging at first to get a clearunderstanding of the business needs but really benefited me at the end. I want to thank the Industry4.0 team for their input and especially my fellow interns that created a great working atmosphere.

As said, this project also is the final chapter of my student life. Friendships are one of the most importantthings in life and I will cherish them forever. Girls, thank you for the great times, motivation and supportyou made the past years Formidable. I also want to thank the people from Interactie, Industria, andESTIEM for the opportunities they gave me to develop myself. Special thanks go to my co-boardiesfrom the 20th board of Interactie. Together we definitely reached new heights.

Without my parents, this result would literally not have been possible. They opened my eyes for theopportunities the technical university offered, which eventually led to me studying Industrial Engineer-ing. Thank you for your unconditional support and always being there for me. The last person I wantto thank is Olaf, my rock for the past five years already. Your listening ear, your joy and your down toearth view on things definitely helped me to complete this thesis.

Nadja Brouns

iv On the road towards Smart Manufacturing

Contents

Contents v

List of Figures vii

List of Tables x

1 Introduction 1

1.1 Problem Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.2 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.3 Research Goal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

2 Research Methodology 4

2.1 Research Paradigms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

2.2 Research Questions and Research Approach . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2.3 Research Scope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

3 Theoretical Background 9

3.1 Architectures in the fourth industrial revolution . . . . . . . . . . . . . . . . . . . . . . . . 9

3.2 Information Systems Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

3.3 The Advanced Flexible Information System Framework . . . . . . . . . . . . . . . . . . . 12

4 Conceptual Design 13

4.1 Architecture Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

4.1.1 Framework for congruent reference architectures . . . . . . . . . . . . . . . . . . . 14

4.1.2 Evaluation by Characteristics of the 4th Industrial Revolution . . . . . . . . . . . 15

4.2 Establishing the Design Principles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

4.3 Design Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

4.3.1 The Three-dimensional Design Cube . . . . . . . . . . . . . . . . . . . . . . . . . . 17

4.3.2 RAMIfication of the AFIS framework . . . . . . . . . . . . . . . . . . . . . . . . . 18

4.3.3 Conceptual framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

5 Operational Framework Design 25

5.1 Operationalizing the framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

5.2 Maturity Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

5.2.1 SIMMI 4.0 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

5.3 Modernized Truijens Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

5.4 The Operational Smart Manufacturing Framework . . . . . . . . . . . . . . . . . . . . . . 28

5.4.1 Elaboration Maturity Level 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

5.4.2 Summary of Maturity Stages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

On the road towards Smart Manufacturing v

CONTENTS

6 Framework Implementation 38

6.1 Implementation Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

6.2 Implementation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

6.2.1 Case-Study 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

6.2.2 Case-Study 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

6.3 Conclusion on Implementation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

7 Evaluation 50

7.1 Evaluation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

7.2 Conclusions and Recommendations for Atos . . . . . . . . . . . . . . . . . . . . . . . . . . 51

8 Conclusion and Outlook 53

8.1 Research Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

8.2 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

8.3 Reflection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

8.4 Future Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

Bibliography 60

Appendix 60

A Design Science Research (DSR) Paradigm 61

B Results from literature review 63

C Analysis of frameworks 68

D The Three-dimensional Design Cube 70

E Setup semi-structured interview 72

F Maturity Models 73

G Smart Manufacturing Framework 74

H Implementation 89

I Evaluation 94

vi On the road towards Smart Manufacturing

List of Figures

1.1 The four industrial revolutions (Thoben, Wiesner & Wuest, 2017) . . . . . . . . . . . . . . 1

1.2 Smart Manufacturing Ecosystem (Lu, Frechette & Morris, 2016) . . . . . . . . . . . . . . 2

2.1 Problem solving cycle, based on van Aken, Berends and Van der Bij (2012) . . . . . . . . 4

2.2 Design Science Research Framework for this Research Project, based on (Hevner, March,Park & Ram, 2004) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2.3 Research Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

2.4 Research process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2.5 Initiatives within the fourth industrial revolution (Brouns, 2019) . . . . . . . . . . . . . . 8

3.1 Architecture as a connector of Business and Technology taken from (Grefen, 2016b) . . . 11

3.2 Three-dimensional design cube (Grefen, 2016b) . . . . . . . . . . . . . . . . . . . . . . . . 11

3.3 AFIS framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

4.1 Positioning Fourth Industrial Revolution Architectures . . . . . . . . . . . . . . . . . . . . 13

4.2 Analysis of a reference architecture (Angelov, Grefen & Greefhorst, 2012) . . . . . . . . . 14

4.3 The four aspects of smart manufacturing, adapted from Wang, Wan, Li and Zhang (2016). 16

4.4 Smart manufacturing scope illustrated using Porter’s Value Chain (Porter, Millar et al.,1985) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

4.5 Three-dimensional design cube (left in 3D, right in 2D), based on Grefen (2016b) . . . . . 18

4.6 The connected enterprise as visualized by Atos . . . . . . . . . . . . . . . . . . . . . . . . 19

4.7 Overview of the product lifecycles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

4.8 Hierarchy dimensions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

4.9 Hierarchy dimension . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

4.10 RAMIfication of the AFIS framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

4.11 Mapping Hierarchy Dimension and Product Lifecycle . . . . . . . . . . . . . . . . . . . . . 22

4.12 Mapping of smart manufacturing characteristics on AFIS framework . . . . . . . . . . . . 23

4.13 High level overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

5.1 Modernized version Truijens Framework (Grefen, 2016b) . . . . . . . . . . . . . . . . . . . 27

5.2 Combining the four framework dimensions . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

5.3 Smart Manufacturing Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

5.4 Maturity Stage 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

5.5 Integration PLM, MES and ERP, adapted from Khedher, Henry and Bouras (2011) . . . 36

5.6 Closed Product Lifecycle, taken from Atos documents . . . . . . . . . . . . . . . . . . . . 37

6.1 Implementation Activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

On the road towards Smart Manufacturing vii

LIST OF FIGURES

6.2 Maturity table for the life-cycle phases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

6.3 Gap Analysis between maturity level 2 and 3 . . . . . . . . . . . . . . . . . . . . . . . . . 41

6.4 Main results Case 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

6.5 AS-IS situation case study 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

6.6 TO-BE situation case study 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

6.7 Main results Case 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

6.8 AS-IS situation case-study 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

6.9 TO-BE situation case-study 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

A.1 Information Systems Research Framework (Hevner, March, Park & Ram, 2004) . . . . . . 61

A.2 Three Research cycles (Hevner, 2007) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

A.3 Knowledge contribution framework (Gregor & Hevner, 2013) . . . . . . . . . . . . . . . . 62

B.1 ISA-95 framework (American National Standard, 2005) . . . . . . . . . . . . . . . . . . . 63

B.2 5C (Lee, Bagheri & Kao, 2015) and 8C (Jiang, 2018) Architecture. . . . . . . . . . . . . . 64

B.3 RAMI 4.0 Framework (VDI/VDE, 2015) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

B.4 Smart Manufacturing Ecosystem (Lu, Frechette & Morris, 2016) . . . . . . . . . . . . . . 65

B.5 Industrial Internet reference Architecture (Lin et al., 2017) . . . . . . . . . . . . . . . . . 66

B.6 Software-Defined IIoT Architecture (Wan et al., 2016) . . . . . . . . . . . . . . . . . . . . 66

B.7 IoT-ARF (Bauer, Boussard, Lucent, Bui & Carrez, 2013) . . . . . . . . . . . . . . . . . . 67

D.1 Three-dimensional design cube, based on Grefen (2016b) . . . . . . . . . . . . . . . . . . . 70

D.2 Visualization of the aggregation levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

D.3 Example of abstraction levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

D.4 Example of realization dimension, taken from Grefen (2016a) . . . . . . . . . . . . . . . . 71

G.1 Process Aspect Basic Digitization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

G.2 Software Aspect Basic Digitization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

G.3 Platform Aspect Basic Digitization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76

G.4 Integration of software and platform aspect Basic Digitization . . . . . . . . . . . . . . . . 76

G.5 Process Aspect Cross-Departmental Digitization . . . . . . . . . . . . . . . . . . . . . . . 77

G.6 Software Aspect Cross-departmental digitization . . . . . . . . . . . . . . . . . . . . . . . 78

G.7 Platform Aspect Cross-Departmental Digitization . . . . . . . . . . . . . . . . . . . . . . . 79

G.8 Integrated software and platform Aspect Cross-Departmental Digitization . . . . . . . . . 79

G.9 Process Aspect Vertical and Horizontal Digitization . . . . . . . . . . . . . . . . . . . . . 80

G.10 Software Aspect vertical and horizontal integration . . . . . . . . . . . . . . . . . . . . . . 81

G.11 Platform Aspect Vertical and Horizontal Digitization . . . . . . . . . . . . . . . . . . . . . 82

G.12 Integrated Aspect Vertical and Horizontal Digitization . . . . . . . . . . . . . . . . . . . . 82

G.13 Process Aspect Full Digitization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83

G.14 Software Aspect Full Digitization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84

G.15 Platform Aspect Full Digitization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85

G.16 Integrated software and platform aspect Full Digitization . . . . . . . . . . . . . . . . . . 85

viii On the road towards Smart Manufacturing

LIST OF FIGURES

G.17 Process Aspect Optimized Full Digitization . . . . . . . . . . . . . . . . . . . . . . . . . . 86

G.18 Software Aspect Optimized Full Digitization . . . . . . . . . . . . . . . . . . . . . . . . . . 87

G.19 Platform Aspect Optimized Full Digitization . . . . . . . . . . . . . . . . . . . . . . . . . 87

G.20 Integrated Software and Platform Aspect Optimized Full Digitization . . . . . . . . . . . 88

On the road towards Smart Manufacturing ix

List of Tables

4.1 List of analyzed reference architectures. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

4.2 Analysis based on 4th Industrial Revolution Characteristics . . . . . . . . . . . . . . . . . 16

4.3 Mapping AFIS abstraction layers and SMF Hierarchy Dimension . . . . . . . . . . . . . . 21

5.1 Summary Maturity Stages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

6.1 Smart manufacturing capabilities linked to maturity model stages . . . . . . . . . . . . . . 41

7.1 Relevance criteria adapted from Shrivastava (1987) . . . . . . . . . . . . . . . . . . . . . . 50

7.2 Evaluation results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

C.1 Multi dimensional space for reference architectures (Angelov, Grefen & Greefhorst, 2012) 68

C.2 Overview of the reference architecture types and their corresponding values . . . . . . . . 69

C.3 Overview of the identification of the RA dimension values . . . . . . . . . . . . . . . . . . 69

F.1 Overview of the Maturity Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

H.1 Smart manufacturing functionalities and supported key capabilities, taken from Lu, Frechetteand Morris (2016). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90

x On the road towards Smart Manufacturing

List of Abbreviations

AFIS Advanced Flexible Information SystemBPMS Business Process Management SystemCAD Computer-Aided DesignCDB Customer DatabaseCPS Cyber-Physical SystemsCRM Customer Relation ManagementDSR Design Science ResearchERP Enterprise Resource PlanningESB Enterprise Service BusI4.0 Industry 4.0IIoT Internet of ThingsIIRA Industrial Internet Reference ArchitectureIoT Internet of ThingsIoT-ARF Internet of Things Architectural Reference FrameworkIT Information TechnologyMES Manufacturing Execution SystemMMS Maintenance Management SystemMOMS Manufacturing Operations Management SystemOEM Original Equipment ManufacturerOT Operational TechnologyPDM Product Data ManagementPLC Programmable Logic ControllerPLM Product Lifecycle ManagementQMS Quality Management SystemRAMI 4.0 Reference Architecture Model Industry 4.0SCADA Supervisory Control and Data AcquisitionSCM Supply Chain ManagementSD-IIoT Software-Defined Industrial Internet of ThingsSIBUS Smart Factory Information Service BusSIMMI 4.0 System Integration Maturity Model Industry 4.0SLM Service Lifecycle ManagementSME Smart Manufacturing EcosystemSMF Smart Manufacturing FrameworkSWD Software Development SystemWHMS Warehouse Management System

On the road towards Smart Manufacturing xi

Chapter 1

Introduction

The world around us is changing rapidly. One of the domains in which transformation takes placeis the manufacturing domain. This transformation, also known as the fourth industrial revolution,is currently an important research area for both business and academia. This research project aimsat developing a framework to support the implementation of smart manufacturing characteristics, bystructuring technologies, systems, and activities. This project is carried out at Atos, a European ITservices corporation.

Chapter 1 introduces the project showing its context, the problem definition and defining the researchgoal consecutively.

1.1 Problem Introduction

Figure 1.1: The four industrial revolutions(Thoben et al., 2017)

An industrial revolution is expected to drastically change themanufacturing domain. This fourth industrial revolution in-cludes concepts as the Internet of Things (IoT), as well asservitization and Cyber-Physical Systems (CPS). It succeedsthe three industrial revolutions that influenced the manufac-turing domain many years ago, shown in Figure 1.1. Tosupport this revolution, countries around the world contrib-ute to research that enables this technology transformation(Thoben et al., 2017). The term ’the fourth industrial re-volution’ suggests that the impact of the revolution is onlyestablished in the industrial production and manufacturingdomain. However, the impact manifests in various aspects ofsociety, including technology, business, and lifestyle (Guop-ing, Yun & Aizhi, 2017). Nevertheless, most research has fo-cused on the impact in global manufacturing and the fourthindustrial revolution is even often referred to as Industry 4.0(I4.0) (Pereira & Romero, 2017).

Industry 4.0 is not the only term related to this revolutionin the manufacturing domain. Due to the increased amount of research in this domain, the concept ofthe fourth industrial revolution is somewhat fuzzy. Terms like smart manufacturing, Industrial Internetof Things and smart factory are country or even industry dependent. Therefore, there is a lack of acommonly accepted definition and a lack of understanding of the core concepts. Researchers do noteven agree with each other if the initiatives are just different names for the exact same thing or theyrepresent a different paradigm. Extending these terms beyond the manufacturing domain leads to evenmore initiatives with similar goals, such as smart home, smart grid and smart city. The diversity ofinitiatives, lack of a common understanding and lack of structure creates a challenge, which causes thisrevolution to still be at an early stage (Kang et al., 2016).

This project focuses on the effect of the fourth industrial revolution in the manufacturing domain,specifically smart manufacturing. Globalization increased the complexity of logistics flows as well asthe growth of international competition, which leads to customers demanding personalized products anda shortened lifecycle of products (Douaioui, Fri, Mabroukki & Semma, 2018). The emergence of the

On the road towards Smart Manufacturing 1

CHAPTER 1. INTRODUCTION

fourth industrial revolution in the manufacturing domain occurs due to the complexity and challenges athand. Smart manufacturing, which is the increased application of collaborative manufacturing systems,throughout manufacturing and supply chain, is one of the focus areas in this worldwide research tocope with the current challenges (Davis, Edgar, Porter, Bernaden & Sarli, 2012; Kang et al., 2016).Corporations can use smart manufacturing to gain a competitive advantage, such as differentiation orcost reduction (Lu, Frechette & Morris, 2016). This means that for companies as well as academia thereis a large interest to further develop and implement smart manufacturing (Davis et al., 2012).

Figure 1.2: Smart Manufacturing Ecosystem (Lu, Frechette & Morris, 2016)

According to Thoben et al. (2017), there are still numerous amount of issues in the field of smart man-ufacturing. One of the issues they point out is the lack of reference architectures, that serve as anabstract blueprint to structure a class of systems (Grefen, 2016b), especially for smart manufacturing.The complexity of the smart manufacturing ecosystem is illustrated in Figure 1.2. It shows three life-cycles, product, business, and production, including various lifecycle steps, as well as the manufacturingpyramid showing the hierarchical production layers. Furthermore, the ecosystem includes systems thatsupport the lifecycle steps and information flows that connect the steps. A reference architecture cansupport in creating the clarity that is needed to connect the various technologies and elements withinthe smart manufacturing paradigm. Currently, there is no framework available that includes standardsor provides structure for new technologies, such as the Internet of Things, cloud manufacturing, but alsosoftware system integration (Lu, Frechette & Morris, 2016). As this is an area of research, the intro-duction of a reference architecture that connects elements from the smart manufacturing landscape tothe lifecycles of the various smart manufacturing dimensions can help to provide structure in a complexenvironment.

1.2 Problem Formulation

The previous section describes the general academic challenge that exists in the fourth industrial revolu-tion. This section describes the challenge in the more concrete context of Atos. Atos is an IT corporationthat provides end-to-end business solutions to help their clients respond to the rapidly changing environ-ment. They offer solutions for smart manufacturing to their clients. Atos recognizes the value of smartmanufacturing and sees that it is at the core of digital transformation in manufacturing organizations.Therefore, Atos is already successfully implementing solutions to help their clients transition towardssmart manufacturing. Nevertheless, they also acknowledge that the lack of standards makes it difficult todetermine which combination of new technologies could most effectively contribute to a changing businessmodel. A reference architecture for smart manufacturing will provide a ”common source of reflection” inorder to guide decisions in the path of digital transformation. Besides, a standard source of informationa framework can also provide another opportunity for Atos. The framework can be used as a tool to helpthe client structure the status of their digital transformation. Without a reference architecture, it can bedifficult and time-consuming to find the gaps within the companies’ smart manufacturing capabilities.By proposing a solution that fills these gaps and helps to raise the smart manufacturing system to thenext level, Atos provides additional value to its clients.

2 On the road towards Smart Manufacturing

CHAPTER 1. INTRODUCTION

Smart manufacturing can help to address many issues that currently play a role in manufacturing en-gineering, such as increased demand and greater variety (Helu, Morris, Jung, Lyons & Leong, 2015).To reach this goal, the manufacturing industry needs assistance from solution providers, such as Atos,to identify the right technologies and to ensure interoperability between new and existing systems tosupport the development of smart manufacturing (Helu et al., 2015). The application fields of smartmanufacturing are numerous and so are the research issues restraining the development of smart man-ufacturing (Thoben et al., 2017). Structuring the knowledge that Atos has gathered as well as bestpractices from literature in a reference architecture can help to identify standardization opportunities,such that the full potential of the fourth industrial revolution can be reached (Helu et al., 2015).

The previous section illustrated the complexity of the smart manufacturing ecosystem but also emphasizesthe potential of the new technologies for the manufacturing domain. However, due to the complexityand lack of standards to provide structure for the ecosystem, it becomes difficult to fulfill this potential.Therefore, the problem statement is defined as follows:

“The enterprises in the manufacturing industry do not have the knowledge to structure the technologies,activities, and capabilities of smart manufacturing, to reach its additional value.”

The problem defined here marks an issue for the entire manufacturing industry. Atos has many clientsin this industry that face this problem and therefore Atos wants to provide a solution to their clients.The problem statement shows the relevance of this study and the need to provide the enterprises with atool that contains the knowledge that gives them the opportunity to pursue smart manufacturing.

1.3 Research Goal

The goal of the research project is to design an information systems reference architecture that cansupport the development of smart manufacturing, by creating structure in the characteristics of smartmanufacturing. According to Grefen (2016b), ”a reference architecture is a general design (abstractblueprint) of a structure for a specific class of information systems”. A reference architecture for smartmanufacturing can help to resolve issues, such as the comparison of use cases, clarify the representationof smart manufacturing and the orchestration of workflows (Thoben et al., 2017). To date, smartmanufacturing research focused on conceptual issues. To reach the full potential, smart manufacturingstrategies must focus on each lifecycle step of a product such as design, operation and maintenance(Kang et al., 2016). Reference architectures are a required element for the application of technologiesin each lifecycle step, to create consistency and standards (Kang et al., 2016). The goal of this researchis to produce a solution, to solve the existing design problem. Based on this goal the design scienceparadigm is used to design an artifact as a solution. Therefore, it is important to state the objective ofthis research. Based on the problem introduction as well as the above-defined problem statement theresearch goal is defined as follows:

“The objective of this research is to develop an information systems reference architecture thatsupports the development of smart manufacturing.”

The deliverable of this study will be an artifact of the type model, as defined by the design science researchterminology of Gregor and Hevner (2013), that shows a representation of the smart manufacturingcharacteristics, which helps enterprises to pursue and develop within the smart manufacturing paradigm.


Chapter 2

Research Methodology

This chapter describes the methodologies that are used to carry out the project and meet the above-defined research goal. The paradigms used to conduct this research are the problem solving cycle byvan Aken, Berends and Van der Bij (2012) and the Design Science Research (DSR) paradigm by Gregorand Hevner (2013). Secondly, the research questions and the research method are defined. The finalsection of this chapter presents the research scope.

2.1 Research Paradigms

To conduct a design science research in a structured manner, van Aken et al. (2012) defined a problemsolving cycle. This cycle consists of the following five steps (1) Problem definition, (2) Analysis &diagnosis, (3) Solution design, (4) Implementation, and (5) Evaluation, (Figure 2.1). These steps mustbe followed precisely throughout the research. Note that the first step of the cycle, the problem definition,is already provided in the previous section of this report.

Figure 2.1: Problem solving cycle, based on van Aken, Berends and Van der Bij (2012)

Besides the problem solving cycle of van Aken et al. (2012) this study also uses the Design ScienceResearch (DSR) paradigm, because the goal is to design an artifact as the solution for the problemdescribed in Chapter 1. DSR is a well-known and fundamental paradigm in Information Systems research(Hevner, March, Park & Ram, 2004). The design framework, shown in Figure 2.2, focuses on the balancebetween relevance and rigor for the design within Information Systems research. The solution design,step 3 in the problem solving cycle of van Aken et al. (2012), corresponds to the design of an artifactin the DSR paradigm. According to Goldkuhl (as cited in Gregor and Hevner (2013)), an artifact is’a thing that has, or can be transformed into, material existence as an artificially made object’(p.341).The DSR paradigm (original shown in Appendix A) is later extended by Hevner (2007) and Gregor and


CHAPTER 2. RESEARCH METHODOLOGY

Hevner (2013). The first extension includes three research cycles to balance the rigor and relevance ofthe conceptual design (Appendix A). The second extension is a knowledge contribution framework toposition the conceptual design (Appendix A).

Figure 2.2: Design Science Research Framework for this Research Project, based on (Hevner, March,Park & Ram, 2004)

Gregor and Hevner (2013) position the DSR knowledge contribution framework, as a tool to position thedeveloped artifact based on two scales: (1) Solution Maturity (i.e. relative to the already known solutions)and (2) Application Domain Maturity (i.e. problem context). The designed artifact in this study canbe positioned within the improvement quadrant (Figure A.3), new solutions for known problems. Morespecifically, information from known I4.0 frameworks is used to provide more complete solutions to solvethe defined problem.

Furthermore, the contribution of the artifact is also dependent on the maturity of knowledge (Gregor& Hevner, 2013). The scale for knowledge maturity ranges from level 1, developed artifacts for specificapplications to level 3, a well-developed theory for general phenomena. The framework as artifactproposed in this study is designed for a more abstract problem and applicable in various situations.Therefore, the artifact is categorized as a level 2 contribution, a nascent design theory.

Finally, the DSR paradigm defines three fundamental research cycles, the rigor, relevance, and designcycle (Hevner, 2007). Each of these cycles has an important role in the successful solution design of anartifact. The rigor cycle ensures innovation by connecting the design activities to the existing knowledgebase, the relevance cycle bridges the practical environment with the design activities and the design cycleiterates between design and evaluation using the requirements from the relevance cycle and informationfrom the rigor cycle. The research cycles are put into the context of this project in the next section.

2.2 Research Questions and Research Approach

To reach the defined end-goal of this study, it is necessary to gather knowledge such that the artifactcan be constructed. Therefore, the following research question is raised:

”How can technologies, capabilities, and activities belonging to smart manufacturing be structured, suchthat it creates new values for the manufacturing industry?”

The defined question consists of two parts that must be answered separately. Firstly, the research willfocus on structuring the various assets of smart manufacturing, to support organizations that want tointegrate collaborative manufacturing systems. In addition, it is important that the constructed artifact,which in design science research is the answer to the research questions, creates value for manufacturers.Therefore, the framework must be evaluated in practice, to investigate if the framework indeed supportsand clarifies the development of smart manufacturing.



To provide an answer to the above-defined question, it is necessary to answer several sub-questions,shown in Figure 2.3. This figure also introduces the research method as it visualizes the link betweenthe problem solving cycle by van Aken et al. (2012), the DSR research cycles by Hevner (2007), and thesub-questions.

Figure 2.3: Research Method

Figure 2.3 shows that RQ1 and RQ2 focus on the rigor cycle. This suggests that the main research isbased on the knowledge base. However, as the design needs to be useful in practice, the research is alsodriven from relevance. This ensures that the eventual design is not only based on academic rigor butalso include relevant aspects from the industry. Furthermore, the answers of RQ1 and RQ2 provide theresults for the second step in the problem solving cycle of van Aken et al. (2012), analysis and diagnosis.The design cycle is an iteration between the actual development of a design artifact and the evaluation.Therefore, RQ3 is divided into three sub-questions, RQ3a, RQ3b and RQ3c. RQ3a provides the basisfor the remainder of the design. The answer to this research question provides the dimensions that serveas an outline on which the characteristics can be positioned. RQ3b will be the result of the first actualdevelopment phase and after evaluation RQ3b is the result of the second iteration. The resulting artifactis the outcome of the solution design step in the problem solving cycle. Lastly the implementation, step4, and evaluation, step 5 are answered by research questions 4 and 5. These research questions are mainlyrelevance driven and supported by the rigor cycle to ensure the link with the already existing knowledgebase.

The fourth industrial revolution triggered many initiatives that are closely related to each other. Theseare terms such as Industry 4.0, smart manufacturing, CPS, Industrial Internet of Things (IIoT) andmany more. As the concepts are all related, frameworks from other initiatives can provide an importantinsight to which elements are important for the Smart Manufacturing Framework (SMF). The SmartManufacturing Framework is the artifact that results from this design science research and can be cat-egorized as a model type contribution (Gregor & Hevner, 2013). The SMF is a higher-level representationincluding the models that form the smart manufacturing reference architecture discussed in Chapter 1.In other words, the reference architecture is an application of the smart manufacturing framework to aset of systems and is included in the SMF (Lin et al., 2017). Both terms are used in this document todefine the artifact. The frameworks from other initiatives are all shortly discussed in Chapter 3, in orderto determine the already existing knowledge base (RQ1). These frameworks all provide different insightsand show a different approach. This makes it important to establish a common ground for the outline



of the smart manufacturing reference architecture, which is also described in Chapter 3.

Once the necessary background is provided the architectures can be analyzed based on their charac-teristics, Chapter 4. For the actual design of the framework, it is important to know which elementsare missing in the existing frameworks and which elements should be included in the reference architec-ture (RQ2). Based on the knowledge gathered from the analysis, an additional design rescope is made.The rescope ensures that the framework only reflects aspects that are relevant for smart manufacturing.Based on this rescope the appropriate dimensions for the outline of the framework can be determined(RQ3a). The design of the framework outline is described in Section 4.3.2. The positioning of the ex-tracted characteristics on these dimensions is described in Section 4.3.3. The conceptual design providesan overview of the smart manufacturing aspects, extracted from the architectures and positioned basedon the defined smart manufacturing dimensions (RQ3b). This shows a high-level unified framework thatpresents the TO-BE state of a smart manufacturing enterprise. RQ3a and RQ3b together form thedesign of the conceptual model, shown as one activity in Figure 2.4. Nevertheless, the framework isdesigned to support the development of smart manufacturing. This asks for an operational frameworkthat visualizes how the TO-BE state can be achieved (RQ3b). The transformation of the conceptualmodel into an operational tool is discussed in Chapter 5. The separation between the conceptual andoperational framework is shown in Figure 2.4.

Figure 2.4: Research process

The resulting Smart Manufacturing Framework is implemented by means of case studies in the manu-facturing sector. The implementation is executed at two enterprises in the manufacturing industry. Animplementation process is established and executed to evaluate if the framework is applicable within themanufacturing industry (RQ4). This method is also known as structural testing (Hevner et al., 2004).The implementation approach is the second artifact of this research and can be identified as a methodtype artifact (Gregor & Hevner, 2013). Successful execution of the implementation process results ina roadmap that shows the smart manufacturing development activities for a company. Furthermore, itprovides insights into the usage of the framework as a tool for Atos. Both the implementation approachas well as the results from the case studies are discussed in Chapter 6.

The operational framework is evaluated during the case studies and described in Chapter 7. The evalu-ation method used is observational (Hevner et al., 2004), the use of the artifact is studied in a businessenvironment. To establish the practical usefulness of the framework the participants of the case studyare asked to evaluate the framework based on the relevance criteria defined by (Shrivastava, 1987).

Finally, conclusions and future research for both the conceptual and operational framework are discussedin Chapter 8. A complete overview of the research process is shown in Figure 2.4.



2.3 Research Scope

Since there is only a limited amount of time available to conduct the research, it is very important todetermine the scope of the research to ensure its feasibility. Therefore, several decisions are made toadjust the magnitude of the research, to ensure the research load is manageable and the research doesnot become too broad. The decisions for scoping are described in this section.

Within the fourth industrial revolution, multiple initiatives are introduced by various organizations. Thevarious initiatives are positioned in a Venn-diagram, shown in Figure 2.5. The Venn-diagram is designedpreviously to this research during the literature review, to provide some clarity and structure for thefourth industrial revolution (Brouns, 2019). The goal is not to create a framework that includes all theinitiatives but to focus on one. The design of the reference architecture includes only elements that can berelated to the smart manufacturing paradigm. The smart manufacturing initiative is less comprehensivethan the Industry 4.0 initiative, as shown in Figure 2.5. The smart manufacturing paradigm doesinclude the entire product lifecycle and mentions the value chain. According to Kang et al. (2016) asmart manufacturing framework should support every step of the product lifecycle. This means that thescope of the framework does include the entire product lifecycle, similar to the product lifecycle shownin Figure 1.2.

Figure 2.5: Initiatives within the fourth industrial revolution (Brouns, 2019)

Furthermore, the main research question shows another scoping decision, namely that the framework isdesigned to be applicable in the manufacturing industry. The manufacturing industry is closely relatedto the smart manufacturing paradigm. Although principles from smart manufacturing might also beapplicable in other domains, the decision is made to focus on the manufacturing industry. This is also arelevant industry for Atos as many of their customers are manufacturers. Nevertheless, a framework forthe manufacturing industry can still imply that the framework should also take care of organizationalstructures or blueprints for production facilities. These aspects are not included in the SMF. Theframework focuses on the integration of information technology and operational technology and thereforebe scoped to an information systems architecture. This scoping decision is also implied in RQ1.


Chapter 3

Theoretical Background

Before the design of the Smart Manufacturing Framework can start, it is essential to determine a com-mon ground. In line with the objective of this research, the theoretical background first discusses thealready existing architectures to establish the knowledge base. The goal of this research is to design anInformation Systems Reference Architecture. Therefore, a formal definition of an information systemsarchitecture is presented and the various types of architectures are discussed. Lastly, the AFIS frameworkis elaborated upon. The AFIS framework is an independent reference framework, focusing on connectingdigital and physical processes and allows for the positioning of other reference architectures. The AFISframework is used as the basis for the framework design.

3.1 Architectures in the fourth industrial revolution

Several industrial organizations, as well as standard development organizations, are creating referencearchitectures for the fourth industrial revolution. However, similar to the initiatives described in theprevious chapter, each of these architectures has its own key concept and viewpoint on the fourthindustrial revolution. Even though there are differences, most of the architectures take into account thefollowing principles (Li et al., 2018):

• Decomposition means the description of multiple dimension within an architecture;

• Focalization the architectures focus on their core concepts and do not include all relevant elementsof the fourth industrial revolution;

• Strategic consistency the architectures are based upon national manufacturing initiatives, suchas smart manufacturing or Industry 4.0.

The architectures that are discussed in this section all results from the literature review prior to thisthesis (Brouns, 2019). Only a summary of the results is given here, to create a basic understanding of thevarious architectures. A visualization of the frameworks and elaboration of the elements is provided inAppendix B. Once a more detailed understanding is deemed necessary for the design of the framework,this will be elaborated upon during that design step.

The ISA-95 architecture is a widely adopted framework for control system integration (AmericanNational Standard, 2005). The manufacturing pyramid, one of the most known visualizations of thestandard, depicts a functional hierarchy model and plays an important role in the establishment of laterframeworks. Although this architecture serves as a proper basis for system integration between theshop floor and enterprise level, it does not include the integration of Information Technology (IT) andOperational Technology (OT). According to (Lu, Riddick & Ivezic, 2016), the functionalities necessaryto establish smart manufacturing are more advanced than the functions defined in ISA-95. Therefore,architectures that focus primarily on the fourth industrial revolution are required for a smooth imple-mentation.

Lee, Bagheri and Kao (2015) propose the 5C architecture, that provides a clear structure for imple-menting Cyber-Physical Systems. The architecture consists of five layers that all propose a step thatwill lead to the implementation of a CPS in a production facility. Nevertheless, the 5C architecturesolely focuses on the vertical integration within a single production facility. It ignores any influences


CHAPTER 3. THEORETICAL BACKGROUND

from the environment. Therefore, Jiang (2018) suggested an extension the the 5C architecture, the 8Carchitecture. By adding three additional facets, content, customer, and coalition, this architecturealso includes the products lifecycle and the value chain. However, the architecture does not provide anyinformation on how these facets should be integrated and applied to an enterprise.

The Reference Architecture Model Industry 4.0 (RAMI4.0) is built to achieve a common un-derstanding between the various industries on standards and use cases for I4.0. Three dimensions thatare all relevant for I4.0 display these fundamental aspects. The framework contains layers, the lifecycleand hierarchy levels (VDI/VDE, 2015). The three-dimensional structure of the framework enables thepositioning of I4.0 standards and helps the identification in overlap and gaps. This approach providesan overview of the already existing and missing standards in the paradigm of I4.0. However, it doesnot provide guidelines on how to establish I4.0 aspects in an enterprise. Furthermore, the integration ofthe various standards created inconsistencies in terms and there is a lack of proper clarification (Frysak,Kaar & Stary, 2018).

The National Institute of Standards and Technology (NIST) constructed the smart manufacturingecosystem (SME). The SME enhances a broad scope of manufacturing systems including product, pro-duction, and business. These three dimensions encompass the manufacturing pyramid, an applied versionof the ISA-95 standard (Lu, Frechette & Morris, 2016). The ecosystem provides a complete overview ofthe various lifecycles and the vertical integration by the manufacturing pyramid. The standardizationof the framework mainly focuses on software applications and the information flows that can supportthe lifecycle processes. Nevertheless, the focus on software applications results in a lack of key tech-nology identifications and implications. This leads to limited improvement in the information systeminfrastructure as concepts such as cloud computing and IoT are omitted (Li et al., 2018).

The Industrial Internet Reference Architecture (IIRA) is designed by the Industrial InternetConsortium, to support the development of Industrial Internet of Things systems (Lin et al., 2017). TheIIRA consists of four viewpoints that show the key aspects of the reference architecture. Each viewpointincludes its own domains, processes, information flows and techniques to support the development of anIIoT system. Even though the architecture itself is not sufficient to fully implement an IIoT system itdoes serve as a good starting point. The architecture is applicable to various numbers of industries dueto its high abstraction level. Furthermore, the lifecycle for both the design and implementation of anIIoT system is included in the architecture (Lin et al., 2017).

The architecture proposed by Wan et al. (2016) is designed to solve the challenge of information sharingwithin the field of IIoT. The Software-defined IIoT architecture (SD-IIoT) visualizes data sharingacross three layers and based on four different devices. It emphasizes the service-oriented approach byimplementing three data related services. Clear communication protocols to transmit data from thephysical layers to the cloud are important aspects. Only in the layer where the cloud is controlled datacan be analyzed and value can be created (Wan et al., 2016). Nevertheless, this framework does notinclude any guidance towards the development of IIoT systems, nor does it include specific standardsor protocols for data transmission and information sharing. The framework mainly suggests that datamust be transferred from a physical asset to a cloud control or other platform.

The Internet of Things Architectural Reference Framework (IoT-ARF) is designed by the IoT-A project (Bauer, Boussard, Lucent, Bui & Carrez, 2013). The IoT-ARF presents a functional overviewof the various components in the IoT paradigm. The framework does not propose any interaction amongthe various components as this can vary based on design decisions. The functional domain model includesthe main assets that must be considered when deploying IoT. The entire framework is more elaboratedthan only the functional view and contains among others an information and communication model.

To identify which elements are relevant for the SMF and what is still missing a more thorough analysisof the framework is conducted in Section 4.1.



3.2 Information Systems Architecture

In general, an architecture is about providing structure. It serves as a blueprint for the design of complexsystems (Grefen, 2016b). In a complex environment such as smart manufacturing, an architecture canbe extremely useful to guide the implementation of smart manufacturing systems. Furthermore, anarchitecture can serve as an instrument to support agility within an organization (Grefen, 2016b). Oneof the goals of smart manufacturing is to improve the agility of the production process (Lu, Riddick &Ivezic, 2016). This means that an organization should be able to respond to the technology push andnot only to the requirements pull. An architecture can be a pivot between the business and organizationrequirements (B&O) on top and technology (T) on the bottom, shown in Figure 3.1. (Grefen, 2016b).

Figure 3.1: Architecture as a connector of Business and Technology taken from (Grefen, 2016b)

The goal of this thesis, as is described in Chapter 1, is to develop an information systems reference ar-chitecture. A reference architecture is used for the design of concrete architectures. Therefore, it shouldserve the design and development of systems in a variety of contexts and projects (Angelov, Grefen &Greefhorst, 2012). According to Bass, Clements and Kazman (2003) a reference architecture is defined as”a reference model mapped onto software elements (that cooperatively implement the functionality definedin the reference model) and the data flows between them”, where the reference model is ”a division offunctionality together with data flow between the pieces”. The border between reference architecturesand concrete architectures can be rather vague because a concrete architecture can be used in multiplecontexts without it being a reference architecture. Therefore, the level of abstraction in reference ar-chitectures is higher than for concrete architectures (Angelov et al., 2012). The standard architecturecan be placed in between, which provides structure for specific architectures in an organization (Grefen,2016b). The abstraction level, the level of detail, is one of the dimensions on the three-dimensional designcube that provides context for the design process, Figure 3.2. The other two dimensions are aggregation,number of elements, and realization often represented using the BOAT description (Grefen, 2016b). Forthe design of the framework, the 3D design cube is used to determine the right levels in the dimensions.

Figure 3.2: Three-dimensional design cube (Grefen, 2016b)



3.3 The Advanced Flexible Information System Framework

The Advanced Flexible Information Systems (AFIS) framework, shown in Figure 3.3, is created toproperly position systems that connect the physical and administrative processes. The integration ofthese two processes is rather difficult, so creating a standard such that similar systems can be comparedand analyzed can support this integration. The conceptual framework, designed by Grefen, Eshuis,Turetken and Vanderfeesten (2018), consists of four layers that represent the abstraction level, wherethe physical layer represents the lowest level and the business layer the highest. At the physical layerthe work is executed. The commands and events of the physical layer are processed in the event layer.The process layer provides a control flow between the tasks of the event layer. Eventually, the businesslayer puts the process into a business context and is considered with the decisions making on a strategic,tactical and operational level. Furthermore, the framework contains two perspectives. The design timeperspective that includes designing and analyzing the models. The second perspective is the executiontime perspective that executes the models and collects the real-time output. The layers and perspectivestogether form the main structure of the framework. Within that structure, data flows and functionalitiesare positioned. The data flows are divided in a descriptive and prescriptive flow. These flows supportthe flexibility that is needed for future information systems. Lastly, the framework shows seven differentfunctionalities. The functionalities show the key capabilities that a framework should possess whenoperating in an agile environment. Among others, the functionalities hold, data gathering, data analysis,and model design. The outline of the AFIS framework serves as a proper starting point for the designof the SMF, as it includes the connection between physical and administrative process, similar to smartmanufacturing, in the abstraction layers and perspectives.

Figure 3.3: AFIS framework


Chapter 4

Conceptual Design

The previous chapter discussed the already existing reference architectures. In this chapter these ar-chitectures are further analyzed. Thereafter, the design principles are elaborated upon, such that thescope of the design is clear. Lastly, the design approach is presented, including the visualization of theconceptual framework.

4.1 Architecture Analysis

The result of the literature review shows that a variety of architectures for the Industry 4.0 paradigmis available. Each of these architectures has its own viewpoint and scope, which makes integration achallenge. In order to determine the relevant aspects of these architectures for the smart manufacturingframework, a thorough analysis is conducted. First, the architectures are analyzed and categorized basedon a framework for evaluating software reference architectures. This provides insights on the type ofarchitecture and the congruence of the architectures, if they fulfill all the criteria. The second analysisis based on the characteristics for the fourth industrial revolution, based on Li et al. (2018).

Figure 4.1: Positioning Fourth Industrial Revolution Architectures

The architectures that are analyzed in this section are shown in Figure 4.1 and result from the literaturereview conducted prior to this master thesis (Brouns, 2019). A few changes have been made comparedto those results, both the ISA-95 and the extended 5C architecture are excluded from the analysis. TheISA-95 framework is not taken into account as it is not a specific framework for the fourth industrialrevolution, rather a well-known standard for manufacturing as a whole. Furthermore, the extension ofthe 5C architecture is rather limited and does not add a significant change to the basic 5C architecture.An overview of the analyzed frameworks including a short description is provided in Appendix B.

The architectures shown in Figure 4.1 are categorized based on the various initiatives within the fourth


CHAPTER 4. CONCEPTUAL DESIGN

industrial revolution. The 5C architecture only supports one of the elements for Smart Manufacturing,the Smart Factory, while the other architectures have a main interest in the implementation of theInternet of Things. Although IoT is one of the key enabling technologies of smart manufacturing, it isonly one of the many aspects that must be addressed. There are only two architectures that include allthe requirements for smart manufacturing, RAMI 4.0 and SME. Nevertheless, it is still worthwhile toanalyze these architectures as they all contain elements that are relevant for the smart manufacturingparadigm.

4.1.1 Framework for congruent reference architectures

According to Angelov et al. (2012) software reference architectures are influenced by three dimensions,the application context, goals, and design. Based on these dimensions a framework is created to evaluatethe level of congruence for reference architectures. An architecture is considered congruent if the abovedimensions are aligned, Figure 4.2a. There are multiple types of reference architectures. Therefore,first a classification must be made before the congruence level of an architecture can be evaluated. Theclassification is based on the three dimensions discussed above. Each of these dimensions is divided intosub-dimensions, which are set by means of interrogatives. This leads to a total of eight questions thatmust be answered, such that the reference architectures can be categorized into five types. AppendixC shows an overview of the sub-dimensions and criteria, Table C.1, and the corresponding referencearchitecture types, Table C.2.

(a) Relationship between reference architecture dimen-sions

(b) Process visualization

Figure 4.2: Analysis of a reference architecture (Angelov, Grefen & Greefhorst, 2012)

The following approach is used for the analysis of the architectures, as is recommended by Angelov et al.(2012): (1) Identify the dimension values for the architecture, (2) Map these values on the types andclassify the architecture, (3) Analyze the architecture, a visualization of the process is shown in Figure4.2b. Executing this process, results in the categorization of the reference architectures and shows thesimilarities between the reference architecture type and the actual reference architecture.

The identification of the dimension values is the first step for analyzing the reference architectures. Basedon the questions one or more of the corresponding criteria were selected as a match for the referencearchitecture. An overview of the answers for each reference architecture is given in Appendix C TableC.3. A choice is made to classify all architectures as classical architecture, instead of a preliminaryarchitecture. Categorizing an architecture as preliminary limits the possibilities for the analysis andwould categorize all architectures as type 5, a preliminary, facilitation architecture. Even though thearchitectures are designed for solutions that are still in a development phase, such as industrial internetand smart manufacturing, many of the aspects in the architectures are known solutions. To obtain themost value of this analysis, the architectures are classified as classical. Another interesting observationis that all of the architectures are designed to be applicable to multiple organizations and are designedby independent organizations. A differentiation between the architectures is made based on the goal forwhich the architecture is designed. Only two of the architectures, RAMI 4.0 and SME, are primarilydesigned for standardization purposes. The remainder of the architectures is designed with the goal toimplement certain technologies and functionalities.



Table 4.1: List of analyzed reference architectures.

Type C1 C2 C3 G1 D1 D2 D3 D45C 3 X ≈ X X ≈ - X -RAMI 1 X - X X X ≈ X -SME 1 X ≈ X X X ≈ - -IIRA 3 X X X X X X X -IIoT 3 X - X X ≈ - X -IoT-ARF 3 X ≈ X X X X X X

Based on the characteristics that were determined in the first step, the architectures are classified basedon the types. RAMI 4.0 and SME are classified as type 1 since both are classical reference architectures,designed for multiple organizations and have a standardization purpose. The other architectures have thesame characteristics for the first two aspects. However, their goal is facilitation hence, these architecturesare classified as type 3.

The analysis of the architectures is shown in Table 4.1. Each reference architecture type has correspond-ing characteristics, these are compared with the determined dimension values from step 1. In case thearchitecture values correspond to the type an X is denoted, when there are some deviations ≈, and ”-”when there is no match. The results show that none of the reference architectures is congruent, althoughthe IoT-ARF is almost congruent except for the, ”who” sub-dimension. The deviations do not implythat the reference architectures are wrong or cannot serve their purpose. Some of the deviations have abigger impact than others.

Based on the analysis it can be concluded that none of the architectures designed for the fourth industrialrevolution are congruent. Still these architectures can provide critical insights in both standardization andfacilitation purposes. Furthermore, the two architectures that comprise all functionalities of the smartmanufacturing paradigm, RAMI 4.0 and SME, are both categorized as standardization frameworks. Thisshows a gap, as there is also a need for facilitation architectures within this paradigm. Most researchis still conceptual and there is only a limited success of real-life implementations (Kang et al., 2016),which provides the opportunity for an architecture that has as main purpose the development of smartmanufacturing.

4.1.2 Evaluation by Characteristics of the 4th Industrial Revolution

Each of the reference architectures is developed by another organization, containing its own viewpointand characteristics that comply with that viewpoint to fulfill specific requirements in the fourth industrialrevolution (Takahashi, Ogata & Nonaka, 2017). In order to create a framework that contains all therelevant aspects of smart manufacturing, it is important to create an overview of the characteristics thatare present in each architecture. The characteristics introduced by (Li et al., 2018) serve as a basis forthe analysis. The most relevant characteristics are categorized according to the four aspects of the BOATframework: Business, Organization, Architecture, and Technology (Grefen, 2016b). Characteristics suchas new materials and new energy are excluded from this analysis, as they are less relevant for a software-oriented architecture. Furthermore, some of the characteristics that Li et al. (2018) identified show ahigh-level of similarities. For example the characteristics organization scope and system hierarchy, bothillustrate an abstraction level within the organization. Therefore, the organization scope is discarded andsystem hierarchy is included in the analysis, as this showed a close relationship with the existing RAMI4.0 framework. The results of the analysis are shown in Table 4.2. Note that the row for Manufacturingtechnology is empty. None of the architectures includes new manufacturing techniques, such as 3D-printor robotics.

The analysis shows that none of the architectures cover all relevant characteristics and there is the needfor a framework that supports the implementation of smart manufacturing on a Business, Organization,Architecture and Technology level. Therefore integration of the characteristics from different architec-tures is necessary to support the development of smart manufacturing. Both RAMI 4.0 and SME includemany of the aspects of the Business, Organizations and Architecture level. Combining elements fromthese two architectures and adding technology characteristics of for example IoT-ARF, would alreadycreate a more complete framework.



Table 4.2: Analysis based on 4th Industrial Revolution Characteristics

Aspect Characteristic 5C RAMI SME IIRA IIoT IoT-ARFBusiness Business model ∼

Product Lifecycle X X ∼Value Chain X X

Organization Performance X XFunctional Layers X X X X

Architecture IT interfaces X ∼ X XSystem Hierarchy X X X XInformation Systems X

Technology ManufacturingData Storage/Processing X X XVirtual (AR/VR) XCommunication ∼ X X

4.2 Establishing the Design Principles

In the second chapter of this report an initial research scope is provided. The initial scope limitedthe research to the smart manufacturing paradigm and to the manufacturing industry. Furthermore,it presented that the framework should only contain information system aspects. This sections furtherrefines the initial scope. Therefore this section provides a concrete definition for smart manufacturing.Furthermore, aspects from the BOAT and AFIS framework described in Chapter 3 are used to providea clear goal and starting point for the design.

First, it is necessary to provide a definition of smart manufacturing that is used as a basis for thedesign of the Smart Manufacturing Framework. The goal of smart manufacturing is to improve quality,flexibility, productivity, and sustainability by he digitizing the manufacturing domain (Lu, Frechette &Morris, 2016). This can be achieved by the integration of physical and digital business processes. Thephysical encloses the actual activities on the shop floor, the production process. The digital embrace theadministrative process, such as customer orders and product descriptions. This goal is also included inthe definition of smart manufacturing that is used as a foundation for the development of the framework(Kusiak, 2018)

“Fully integrated, collaborative manufacturing system that responds in real time to meet changingdemands and conditions in the factory, in the supply network and in customer needs”

The definition above includes the broad scope of smart manufacturing stretching beyond a single factorythroughout the life cycle of a product and its supply chain. This approach for smart manufacturingis closely related to the approach of Industry 4.0. According to Kamble, Gunasekaran and Gawankar(2018) vertical integration is necessary to enable the above-described requirements for a single factory.Horizontal integration is required to support the entire supply chain network. Lastly, the combinationof the two leads to the end to end digital integration, to fulfill the customers’ needs and support theproduct along the lifecycle. These three features together enable the smart manufacturing paradigm.

Figure 4.3: The four aspects of smart manufacturing, adapted from Wang, Wan, Li and Zhang (2016).



Besides these three aspects an equally important but often forgotten aspect is the use of new technologies.The importance of this aspect is emphasized by the following statement, Moeuf et al. (2018) claimseach definition for Industry 4.0 includes the use of new technologies. The same holds for the smartmanufacturing paradigm, where IoT is considered as one of the main enablers (Yao et al., 2017). Forthis reason, the technology aspect should be included as the fourth feature that enables the smartmanufacturing paradigm. The scope of smart manufacturing is visualized in Figure 4.3

It is possible to translate these aspects into activities and divisions within an enterprise. For a man-ufacturer, the term production is more tangible and more used, than vertical integration of a facility.Porter’s value chain is a well-known and often used method to show the various aspects of an enterprise(Porter, Millar et al., 1985). Figure 4.4, shows the functions that are in scope based on Porter’s valuechain. The green functions represent the scope, the grey functions have a direct link to the scope andthe red functions are out of scope.

Figure 4.4: Smart manufacturing scope illustrated using Porter’s Value Chain (Porter, Millar et al.,1985)

Second, the design process for the framework focuses mainly on the architecture level within the realiza-tion dimension, the A within the BOAT framework. This decision is made due to the limited time of theproject. In addition, the architecture dimension is expected to be the most relevant, as the positioningof software systems and their connectivity is an important part of smart manufacturing. Nevertheless,as the goal is to design a complete reference architecture, aspects from the other levels within the BOATframework will be partially included in the model and receive attention.

Third, as a starting point for the design of the SMF, an independent reference architecture is used. Theframework for Advanced Flexible Information Systems is already introduced in Section 3.3 and serves asa good starting point. It is designed to position already existing architectures and can, therefore, be usedas a tool in the design process. It is specifically designed to position systems that connect the physical andadministrative processes. Smart manufacturing is also an integration of a physical production processesand administrative processes such as design and order systems. For this reason, the AFIS framework isexpected to be a useful tool to map the characteristics of smart manufacturing.

4.3 Design Approach

In this section of the report, the design of the conceptual framework is described. First, the use of the3D design cube is discussed. Second, the AFIS framework is adapted to the fourth industrial revolution.Lastly, the conceptual framework is presented.

4.3.1 The Three-dimensional Design Cube

As was mentioned in Section 3.2, the three dimensional (3D) design cube of Grefen (2016b) is used todetermine the right dimensions for the framework design. Figure 4.5 visualizes the three-dimensionaldesign cube in the 3D version (left) and 2D (right). In the 2D version the colored block depicts the



architecture model at a specific level of aggregation, abstraction and realization (Grefen, 2016b).

Figure 4.5: Three-dimensional design cube (left in 3D, right in 2D), based on Grefen (2016b)

During the design of an architecture, a design space is traversed (Grefen, 2016b). The 3D design cubeprovides a design space using three dimensions, aggregation, abstraction, and realization. The aggrega-tion dimension determines the level of detail, abstraction is the level of concreteness and the realizationdimension follows the BOAT-framework.

The number of levels within every dimension of the design cube can be specified for every project.This project uses the 3D cube consisting of 3x4x4=48 cells, as visualized in Figure 4.5. Due to timerestrictions, it is not possible to traverse the design cube. Traversing the design cube would lead to anew architecture design for each of the steps and drastically increases the number of models. Therefore,the design is positioned at one cell within the cube. The design of the Smart Manufacturing Frameworkis positioned on aggregation level 3, i.e. main structure contains components, abstraction level 2, i.e.specific system types and the architecture level of realization. The levels are described more specificallyin Appendix D.

4.3.2 RAMIfication of the AFIS framework

The AFIS framework is designed to position any flexible information system and is not specific for thefourth industrial revolution. The focus of this research is the manufacturing domain. Therefore, theoutline of the framework should incorporate aspects that can easily be related to this domain. Designingthe basis of the framework based on principles from the manufacturing industry makes it recognizablefor the intended end-users. Furthermore, it ensures that the smart manufacturing capabilities are placedwithin the right context.

According to Kang et al. (2016), a Smart Manufacturing Framework should support all the steps inthe product lifecycle process. This holds that the application of systems and technologies is necessaryfor each of the lifecycle steps and interoperability of the heterogeneous systems is required. Supportingthe product lifecycle is part of the design scope and matches with one of the four key aspects of smartmanufacturing, End-to-End Integration. There are many variations of product lifecycle management,varying in the number of lifecycle phases and all displaying different terms (Kiritsis, 2011; Stark, 2015;Subrahmanian, Rachuri, Fenves, Foufou & Sriram, 2005).

The product lifecycles are included in both the RAMI4.0 and SME architectures. The lifecycle of SMEis a bit more elaborated and consists of 6 phases, where the RAMI4.0 lifecycle only consists of 4 phases.The Smart Manufacturing Framework will eventually be used as a tool. Therefore, it should be easy touse and the elements must be recognizable. The goal of the framework is to be generic and applicableto any production enterprise. Nevertheless, the tool should mainly serve Atos’ clients, so it should be inline with the best practices Atos already uses. A visualization of one of these best practices is providedin Figure 4.6. The product lifecycle steps defined by Atos are only three, design, make and use/service.

An overview of the product lifecycles is provided in Figure 4.7. The figure positions the phases ofthe various lifecycles relative to each other. The lifecycle at the bottom visualizes the phases that areincluded in the Smart Manufacturing Framework. The decision is made to integrate the RAMI4.0 andAtos lifecycles to create a new lifecycle. This led to a lifecycle that is still generic, so not just specifically



Figure 4.6: The connected enterprise as visualized by Atos

designed for Atos, but has a strong connection with the best practice of Atos. This makes it a recognizablelifecycle for the intended users, as well as any manufacturing company.

The most important change in the lifecycle is to replace the design phase with the development phase.The development phase is more comprehensive and also includes activities after the initial design ofthe product, such as production engineering and version control. After evaluating the lifecycle withmultiple employees at Atos it was deemed unnecessary to split the development phase. This would beharmful to the accurate positioning of the smart manufacturing characteristics and the readability of theframework. The production and usage/service phase are both similar to the phases in the other lifecycles.The production phase considers making the actual product and the usage/service phase includes servicesand activities for the product in use. The recycling phase that is shown in SME is excluded from thelifecycle, as it does not provide enough value for the lifecycle dimension and the placement of smartmanufacturing capabilities.

Figure 4.7: Overview of the product lifecycles

According to Li et al. (2018) there are two suitable dimensions for a Smart Manufacturing Framework.The lifecycle, as was just discussed, and enterprise hierarchy levels. The enterprise hierarchy levels arealso part of the RAMI4.0 framework and describe a certain level of abstraction. The hierarchy levels inRAMI4.0 follow the IEC 62264 and IEC 61512 standards, also known as the equipment hierarchy modelfrom the ISA-95 and ISA-88 standards. The two hierarchy dimensions are shown in Figure 4.8.

The RAMI levels are based on the ISA-95 and ISA-88 standards, but also show quite some differences.For the design of the Smart Manufacturing Framework, the two models are combined. However, thehierarchy equipment model is used as a basis, as this standard is already well established and oftenevaluated. An important note is that the visualization shown in Figure 4.8b, is the integrated view ofboth the ISA-88 and ISA-95 model. The ISA-88 standard focuses on batch control production, whilethe ISA-95 standard also includes, continuous production, discrete production, and storage. The main



(a) RAMI4.0 Hierachy Dimension (VDI/VDE, 2015)(b) Hierarchy Equipment model ISA88/95 (Scholten,2007)

Figure 4.8: Hierarchy dimensions

focus of the framework is the discrete manufacturing industry. Therefore, the elements from the ISA-88standard, equipment module and control module, are out of scope.

Figure 4.9:Hierarchydimension

The work cell is the lowest hierarchy level. The equipment in a factory can be po-sitioned here to execute simple activities, for example, the sawing machine or paint-ing station. Integration of the work cells creates a production line. A productionline can produce multiple items in a row and execute multiple functionalities, forexample, an assembly line. A site is the actual physical factory and involves pro-duction lines and cells to produce a (half)-product. The activities for a site includeproduction planning and scheduling, which are higher level activities than the ac-tual production itself. Note that the area is not included. Due to the small dif-ference between area and site, it might become unclear where to position some ofthe smart manufacturing characteristics. On top of the site is the enterprise, thehighest hierarchy level in the ISA-95 model. An enterprise can consist of multipleproduction facilities, sites, and strategic decisions are made on this level, such aswhat product will be manufactured where. RAMI 4.0 added another level on topof the enterprise, connected world, to illustrate that I4.0 activities stretch beyonda single enterprise. The same holds for smart manufacturing. Therefore, the de-cision is made to add a hierarchy level on top of the enterprise level. The connec-ted world is a bit vague and includes a large scope. This large scope is not ne-cessary as an enterprise does not have to be connected to the entire world. It isimportant for an enterprise to stay connected with their own network of suppliersand customers (Carvalho, Chaim, Cazarini & Gerolamo, 2018). Thus the networkis considered to be the highest hierarchy level. RAMI 4.0 also connects two hier-archy levels to the bottom of the ISA-95 model, the product and field device levels.Both are not included in the hierarchy levels of the Smart Manufacturing Frame-work. The field device level, includes for example smart sensors. The assumption ismade that these sensors are either part of the equipment in the work cell layer orpart of a product. The product layer is also omitted because the product requiresits own lifecycle. According to Frysak et al. (2018), it is considered a pitfall whenthe product is also an integral part of the plant, thus it is not included as a separ-ate hierarchy level. The visualization of the hierarchy levels is presented in Figure4.9.

The hierarchy and lifecycle dimension serve as the outline for the new, smart manufacturing oriented,AFIS Framework. Mapping those two dimensions on to the AFIS framework creates the basis for theSMF. This mapping is visualized in Figure 4.10 and the end result is shown in Figure 4.10b. Originally,the AFIS framework consists of a design and execution time perspective. The design perspective isreplaced by the development phase, that holds the same functionalities, such as analysis by simulationand the design functionality. The execution perspective is divided into two phases, the production phase



(a) Hierarchy and Lifecycle Dimensions (b) Adapted AFIS framework

Figure 4.10: RAMIfication of the AFIS framework

and the usage/service phase. The production phase holds very similar functionalities to the executionperspective, such as gathering the data from the shop floor, adjusting machines and planning based on theanalysis and the actual control. The usage/service phase is a bit more difficult to define. In this phase,real-time data is gathered from the product in use, as well as the gathering of customer related data andcustomer orders. The latter does not have to be real time. The abstraction layers of AFIS are replacedby the hierarchy dimension. The hierarchy dimension also considers a certain level of abstraction. Thelowest abstraction level, the work cell, executes the physical work and is more hardware-oriented. Onelevel higher the events from the work cell layer are processed in the context of a production line. Onthe highest level of abstraction information is processed in the context of a collaborative value network,such that they relate to the business goals the network determined. A mapping of the AFIS abstractionlayers and the hierarchy dimension of the SMF is provided in Table 4.3.

Table 4.3: Mapping AFIS abstraction layers and SMF Hierarchy Dimension

AFIS Abstraction Layers SMF Hierarchy DimensionDescription Layer Layer DescriptionX X Network Network integration beyond

enterpriseRelate process effects to abusiness context

Business Enterprise Strategic decision makingand planning

Creating a control flowbetween tasks

Process Site Structure activities forproduction planning

Processing activities ofPhysical Layer

Event Production Line Integrate functionalities ofwork cells

Physical assets gather dataand execute tasks

Physical Work Cell Equipment executing basicactivities and gathers shopfloor data

Before the smart manufacturing characteristics can be positioned on the framework, it is necessary toconnect the hierarchy levels and the lifecycle phases. The hierarchy levels are based on a manufacturingstandard, which relates closest to the production phase. Nevertheless, it must be possible to accuratelyposition elements on any level in any phase. Therefore, for each cell the meaning is defined, based onthe relationship between the hierarchy and lifecycle dimension. Providing a common guideline for themapping helps to position the artifacts correctly. For the cells in the usage phase on site and productionline level, no definition is provided. The assumption is made that the levels and phase hold no clearrelation, mainly because no elements were found in real-life scenarios that fit these cells. In the productionphase at the work-cell level the sensors are attached to the machines and data bout the physical processis generated. A similar approach for the product in use is used, the sensors are included in the productand data is generated from the product. The relations are described in Figure 4.11.



Figure 4.11: Mapping Hierarchy Dimension and Product Lifecycle

4.3.3 Conceptual framework

Now the AFIS framework is adapted to the fourth industrial revolution, the smart manufacturingcharacteristics can be mapped on the new version of the AFIS framework, AFIS/4IR. Therefore, themain information system characteristics of the evaluated architectures are extracted and positioned onAFIS/4IR. A visualization of the positioning is shown in Figure 4.12. The various shapes and colorsshow to which architecture the characteristic belongs, displayed in the legend at the bottom of the figure.

All the extracted elements can be related to one of the I4.0 characteristics discussed in Table 4.2. Aspectsof the analyzed architectures that could not be related, were not taken into consideration for the posi-tioning. Some of the elements were easier to position than others, mainly due to the concreteness of theelement or the positioning within its own architecture. For example, the software components extractedfrom the Smart Manufacturing Ecosystem are rather concrete and already positioned along a productlifecycle. Additionally, the elements only must be positioned on the hierarchy levels. The equipmenthierarchy can be linked to the functional hierarchy of ISA-95. The functional hierarchy divides elementssuch as Enterprise Resource Planning (ERP), Manufacturing Execution System (MES), and Supervis-ory Control and Data Acquisition (SCADA), based on their functionalities (Erasmus, Vanderfeesten,Traganos & Grefen, 2018). A direct mapping can be made with the equipment hierarchy. Therefore, themanufacturing pyramid can be recognized within the production phase, field device for sensing and ma-nipulating, SCADA for Control, Manufacturing Operations Management (MOM) for workflow control,and ERP for enterprise scheduling. The Product Lifecycle Management (PLM) system is considered tobe at a similar hierarchy level as an ERP system, as it considers a long term focus. Furthermore, itstretches along the entire product lifecycle, according to the visualization of the SME (Lu, Frechette &Morris, 2016). The other elements of the SME are positioned using a similar approach.

The identification of relevant elements in the Industrial Internet Reference Architecture, was more dif-ficult, due to the high abstraction level and a high-level description of viewpoints (Lin et al., 2017).Therefore, only four relevant elements could be extracted. These elements are all positioned within theproduction phase, as that is considered the main application domain for the Industrial Internet. Thebusiness view and usage view are both high-level viewpoints, directly visible in the IIRA. The businessviewpoint includes business-oriented concerns and is therefore positioned on an enterprise level. Theusage view considers the actual activities to solve those business concerns and is positioned one levellower in the hierarchy. Both the operations/information and control elements are extracted from thefunctional domain model, one aggregation level higher than the functional viewpoint. The control func-tionality enables the sensing and actuation of the physical system, similarly to the field device, and ispositioned on the work cell level. The operations and information domain provide the data collectionfrom various business applications, data modeling, data analytics and provides the user interfaces. These



activities go beyond enterprise borders, thus the operations and information domain are positioned onthe network level.

Figure 4.12: Mapping of smart manufacturing characteristics on AFIS framework

The main focus of the 5C architecture is, similarly to the IIRA, within the production phase, as it isdesigned to develop a CPS within a factory (Lee et al., 2015). However, some of the high-level elementscan also be related to the entire lifecycle such as data conversion and simulation. Therefore, theseelements are stretched across the lifecycle and marked at the right side of the framework, to show thatthey affect the entire hierarchy level. The sensor network in the smart connection level can operate onthe shop floor, but also on a product in the usage/service phase. The configuration level contains theself adjust functionality of machines and stretches from enterprise to work cell. Decisions made on theenterprise level automatically affect the machines and systems on a lower level. The conversion levelexecutes component based, low-level analytics on a production line level. The cyber level is positionedone level higher as it concerns the clustering of the machine data. Lastly, diagnostics and decision makinghappens with the cognition level, which is an enterprise-wide functionality.

The Software-Defined IIoT architecture focuses mainly on the integration between the physical, controland application layer using industrial bus or cloud, either private or public (Wan et al., 2016). The cloudis positioned on a network level, to improve the availability of service beyond the enterprise and ensurestandardization. The BUS is used to connect the systems enterprise-wide.

The IoT-ARF provides a complete overview of IoT implementation, but this report only considers thefunctional model (Bauer et al., 2013). Mainly the service-oriented aspects are valuable for the positioningwithin the SMF. According to Vandermerwe and Rada (1988) services become increasingly importantfor industries and a rising number of companies shifted there focus from goods to services. Services arealso considered to be the central factor of economic growth, which would clarify the interest of companies(Ehret & Wirtz, 2010). The IoT-ARF uses an IoT communication layer on the work-cell level for theconnectivity between sensors and actuators. The process management and execution are positioned on asite level. The notion that the enterprise should be a service organization is stretching across the productlifecycle. And lastly the IoT service is a service that is offered towards the customer, thus positioned ona network level in the usage/service phase.

The positioning of the smart manufacturing characteristics provides an overview of the aspects fromvarious architectures. Nevertheless, it still displays a scattered ecosystem and might also cause confusiondue to the high amount of elements. To overcome this a high-level overview is presented that containsthe main functionalities within the smart manufacturing paradigm, Figure 4.13. This high-level overviewpresents the goal of an enterprise that wants to become a smart manufacturing enterprise. The digitalproduct twin can improve the process of product development, based on virtual and physical data.This can not only lead to improvements in the product development phase, but along the product



Figure 4.13: High level overview

lifecycle (Tao et al., 2018). The smart factory is a core concept within smart manufacturing and issupported by the Internet of Things and CPS (Yao et al., 2017). The goal of the smart factory isto increase the intelligence and flexibility of a plant to become more cost-effective. The integration ofmachines products and resources should improve the self-optimization of the production process and meetcustomer requirements by enabling mass customization (Douaioui et al., 2018). The connected customeris necessary for the enterprise to quickly respond to the fast changing customer demands and to allow formass customization. This is all coordinated, by the integrated enterprise information systems along theproduct lifecycle and enabled by a service-oriented business. On a network level smart manufacturingstrives for an integrated supply chain and an agile collaborative network (Santos, Mehrsai, Barros, Araujo& Ares, 2017).

The conceptual framework visualized in Figure 4.12 provides an integrated overview of the importantcharacteristics within the smart manufacturing paradigm. The characteristics are mapped on an in-dependent framework using two relevant axes. The mapping on an independent framework, shows aunified model, including elements from various viewpoint. A unified model for smart manufacturingshould benefit users with a central and complete value, instead of the specific functionalities each indi-vidual reference architecture now offers (Takahashi et al., 2017). It indicates which elements should bepresent within an enterprise that pursues a smart manufacturing journey. Furthermore, it shows thatenterprise information systems provide an important basis for the success of smart manufacturing, whilethis is often not considered to be a key aspect like IoT and CPS (Pereira & Romero, 2017; Thoben et al.,2017).

An important shortcoming of this framework is the low level of operational capabilities. The frameworkonly provides a complete picture of all the elements that are needed for smart manufacturing, as well asa high-level overview of the ideal situation. It can not directly be used as a tool by Atos to show clientsthe opportunities that the smart manufacturing paradigm can offer. Furthermore, it does not include aguideline how an enterprise can reach the ideal situation. This is especially important, as the initial goalof the framework is to support the development of smart manufacturing. For this reason, this conceptualframework design should be transformed into an operational framework that contains activities and canbe used to design a roadmap to ensure the feasibility of the smart manufacturing journey.


Chapter 5

Operational Framework Design

In this chapter the conceptual framework as introduced in the previous chapter is transformed in toan operational framework. The conceptual framework is here referred to as the α-version, and theoperational framework as the β-version. The α and β approach is based upon a well-known testingstrategy (Sawant, Bari & Chawan, 2012). The α-model is used for in-house testing to examine whetherthe main outline was useful and the characteristics were recognizable. During meetings with possibleusers, the outline of the conceptual framework proved to be useful, but the overall approach appearedto be too conceptual. The decision is made to create a β-version that is operational and can be used inthe industry. The β-version is an extension of the α-version. In other words, the outline of the α-model,i.e. the hierarchy levels and product lifecycle, serves as a basis for the β-version.

First, the reason to use a maturity model as an extension for the β-version is discussed. Thereafter,the two main design tools, the maturity model and the modernized Truijens Framework, are elaboratedupon. Lastly, this chapter shows the design of the operational framework.

5.1 Operationalizing the framework

There are multiple approaches that can be used to extend the α-version of the framework and create anoperational tool. However, from a relevance perspective, extending the framework by maturity stages,is considered to be most beneficial. Based on expert interviews it was concluded that there is a needwithin businesses for development plans and roadmaps. Companies are eager to see a variety of stagesand activities that help them to visualize and proceed with their smart manufacturing journey. Thisdesire can be seen all over the world as technology roadmaps on both industrial and national level havebeen designed to support the fourth industrial revolution (Santos et al., 2017).

According to Weber, Konigsberger, Kassner and Mitschang (2017), the fourth industrial revolution ismainly data-driven. Enterprises that want to take advantage of this revolution need to change theirIT infrastructure. To make this change happen companies must be able to assess their current ITlandscape and define a target architecture. Based on already existing reference architectures, like theones presented in Section 3.1, features can be extracted and divided into levels to create a maturitymodel. The extraction of elements is already executed in the previous chapter. Now, these features canbe categorized to form the various maturity stages.

5.2 Maturity Models

The reason to operationalize the framework by extending the framework with a maturity model isdescribed above. There are a number of maturity models that can be used to create the Smart Manufac-turing Framework. In this section, the various types of maturity approaches are discussed. Afterward,an insight is given in the already existing maturity models within the fourth industrial revolution. Thematurity models are analyzed based on their fit with the purpose of this research and useful elementsare extracted.

Maturity is the process of developing from an initial state towards a final state (Fraser, Moultrie &Gregory, 2002). This definition introduces the important concept of maturity stages. These stages haveto be overcome before the end state is reached and are sequential and hierarchical (De Carolis, Macchi,


CHAPTER 5. OPERATIONAL FRAMEWORK DESIGN

Kulvatunyou, Brundage & Terzi, 2017). Fraser et al. (2002) defined three types of maturity models herearranged in complexity, (1) Likert-like questionnaires, (2) maturity grids, and (3) CMM-like models, thatall contain maturity stages. The CMM-like models are the most structured and provide clear guidancethroughout the maturity levels, which is especially important when creating a roadmap. Therefore, theCMM structure serves as a guideline for designing the maturity model.

A literature review provided a list of 10 maturity models that are related to the fourth industrial re-volution. The list can be found in Appendix F. The aim of the list is not to show completeness, butrather aim for a good representation of the variety in maturity models. The number of stages in thematurity models are either 3, 5 or 6. The focus of the maturity models ranges from smart factories toenterprises and also distinguishes between large enterprises and a Small and Medium-sized Enterprises(SME). It can also be noticed that a limited number of insights are given on specific activities, systemimplementation or IT infrastructure in any of the maturity models. Especially the maturity models thatconsider multiple organizational dimensions and focus on a broader scope have a very high-level approachshowing little concrete content.

One of the maturity models uses an approach that is rather similar to the purpose of the Smart Manu-facturing Framework. The System Integration Maturity Model Industry 4.0 (SIMMI 4.0) focuses on thesoftware landscape of a company, which aligns with the information systems architecture approach forthe design of the SMF. Furthermore, the framework uses the four key aspects of smart manufacturing,horizontal integration, vertical integration, end-to-end integration and technological support as the fourmain dimensions on which maturity can be reached (Leyh, Bley, Schaffer & Forstenhausler, 2016). Thedimensions and software focus correspond well to the scope that was determined in Section 4.2. Basedon the analysis of the maturity models it is decided that the stages and dimensions of SIMMI 4.0 serveas a basis for the design of the β-version. A further elaboration on the SIMMI 4.0 maturity model isprovided in the next section.

5.2.1 SIMMI 4.0

As was mentioned in Section 5.1 companies need to assess their current IT landscape to change theirIT infrastructure according to smart manufacturing needs. SIMMI 4.0 is a tool that can be used toclassify the IT system landscape against these needs (Leyh, Bley, Schaffer & Forstenhausler, 2016).The model consists of the four key aspects of smart manufacturing that were determined in Section 4.2.Furthermore, the model consists of five maturity stages. The number of stages is justified by the fact thatthe smart factory is completed in the middle stage. The smart factory is one of the core concepts of smartmanufacturing and an important milestone within the maturity stages (Yao et al., 2017). According tothe SIMMI approach, a company does not need to reach full maturity for all the dimensions. Dependingon their business goals one dimension can be more important than another. The various maturity stagescan be summarized as follows:

1. Basic Digitization None of the requirements are met, the company is still orienting. Processesare not digitized and information is siloed.

2. Cross-departmental digitization Digitization has been implemented across departments andcan partially be exchanged automatically. The company no longer exists of data islands. Enterprisesystems are supporting both production and product development. Data exchange is not yetautomatized, so previous and following process steps are not optimized.

3. Vertical and Horizontal digitization Industry 4.0 requirements are implemented enterprise-wide and services are available throughout the company by cloud solutions.

4. Full digitization Company is integrated into a value network, integration is enterprise-wide,cross-corporate horizontal and vertical. Information is automatically passed through the productlifecycle.

5. Optimized Full digitization All industry 4.0 activities within the company and across companyborders are optimized. New business models are created based on the collaborations in the valuenetwork.



An additional benefit from the SIMMI model is that the model is validated by case studies. Based on aself-assessment the results showed that the maturity stages that were defined in the model, matched withthe real-life situation of companies (Leyh, Bley, Schaffer & Bay, 2017). The activities that are defined toreach the next maturity stage are relatively abstract and it is hard to create a concrete roadmap basedon the model. Furthermore, the maturity model does not include any visualizations or suggestions whichenterprise systems should be integrated. Including these aspects would make the tool more pragmatic.

5.3 Modernized Truijens Framework

In Section 4.3 three dimensions for the architecture design were defined: the abstract, aggregation andrealization dimension. These are all explicit dimensions shown in the 3D design cube (Grefen, 2016b).An additional and implicit dimension of the design cube is the aspect dimension. The aspect dimensiondefines the way someone looks at the architecture, it provides various viewpoints. It also determineswhich characteristics are included in the architecture. This dimension becomes important during theoperationalizing of the framework because each maturity stage is a self-contained model that can consistof multiple aspects. To determine the aspects that are relevant for the SMF, the modernized version ofTruijens Framework is used (Grefen, 2016b). The Truijens Framework defines 5 different views withinthe aspect dimension; 1) the data aspect describes the organization of data within information systems,2) the process aspect describes the organization of processes in an information systems, 3) the softwareaspect describes the software modules and its connections, 4) the platform aspects describes underlyingsoftware and hardware in terms of computing and network facilities, finally 5) the organization aspectdescribes how the information system is embedded into an organization (Grefen, 2016b).

Due to a limited amount of time, it is not feasible to describe each of the aspects of the TruijensFramework. Therefore, three aspects that were considered most valuable and distinctive are chosen.Each maturity stage is described by the following three aspects, the process aspect, to provide a high-level overview of activities, the software aspect, to describe the information systems that are necessary,and lastly the platform aspect, that shows the integration between the modules. According to Li et al.(2018), the integration of information technology, industrial technology, and management technology isthe core consideration for manufacturing enterprise. For that reason, the software aspects and platformaspects are chosen. The software aspect is a way to display the mentioned technologies, while theplatform can visualize the integration. The process aspect helps to provide a high-level understandingof the business functionalities. The data aspect is also considered relevant to the smart manufacturingparadigm. However, describing all the data types, content, and flows would be too time-consuming.Additionally, the organization aspect is also not included in the framework. Similar to the data aspect, theorganization aspect is also an important element for an enterprise to succeed in their smart manufacturingjourney. Nevertheless, the organization aspect is very depending on a specific situation and alreadyexisting organization structure. Both the data and organization aspect are out of scope.

Figure 5.1: Modernized version Truijens Framework (Grefen, 2016b)



5.4 The Operational Smart Manufacturing Framework

In this section, the design of the operational Smart Manufacturing Framework is discussed. First, theresult of the overall design is presented. Thereafter, an elaborate example is given for one of the maturitystages. Lastly, a summary for each of the stages is presented and some of the elements are discussed inmore detail.

The Smart Manufacturing Framework consists of four dimensions. The α-model designed in Chapter 4 isused as the basis for the development of the β-model. Therefore, the hierarchy dimension and the lifecycledimension are directly plotted on the SMF. This basis is extended by the maturity dimension, definedby Leyh, Bley, Schaffer and Forstenhausler (2016) and the selected aspects of the Truijens framework.When combining these dimensions a four-dimensional framework is created, the visualization is shownin Figure 5.2.

Figure 5.2: Combining the four framework dimensions

It is not easy for the human brain to understand a four-dimensional space, nor does it look visuallyappealing. For this reason, a similar approach is taken to the one of the 3D Design cube of Grefen(2016b). The four-dimensional space is reduced to a three-dimensional space. The hierarchy, lifecycle andmaturity dimensions stay explicit, making the aspect dimension implicit. The implicit aspect dimensionis not a visual part of the framework but stays hidden. This results in the reference model shown inFigure 5.3. The hierarchy levels are plotted on the vertical axis and display the exact same levels as inthe α-model. The product lifecycle as presented in Section 4.3.2 is positioned on the left horizontal axis.The right horizontal axis shows the extension for the operational model and displays the descriptors ofthe maturity stages. These descriptors are the same as the ones used in the SIMMI 4.0 maturity model.The intention is to stay as close to the stage description of SIMMI 4.0 as possible, as this framework isalready assessed and validated. However, while designing the second maturity stage and discussing itwith intended end-users and experts in the field, it was decided to adapt stage two.

The SIMMI 4.0 maturity model describes the second stage as digitization across departments, cross-departmental digitization. However, the description mentions that information can only be exchangedautomatically among different departments and business areas. Based on discussions with experts it wasdecided this is not a likely situation, comparing it to actual cases. Companies first ensure that their owndepartment is digitized and later have integrated systems across departments. Therefore, maturity stagetwo is defined as follows, departments are fully digitized and there are no data islands within departmentsanymore. Data sharing across departments is done actively but not yet automated, it is still based onrequests. Another change that is made in the necessity of cloud-based solutions in maturity stage 3 andstage 4. The main point here is that the various systems should be integrated and accessible throughoutthe enterprise and beyond. This means that there should at least be middleware that integrates theinformation systems, but it is less important where it is integrated. This can occur in the cloud, whichimproves standardization purposes, but can also be in a locally controlled data center. For this reason,the cloud is merely a possibility to position the system, but the middleware is a necessity to enable thefunctionality. Lastly, SIMMI 4.0 suggests that product design and development are not digitized in stage1. Again experts say that this is not the real-life situation, because almost every company designs usingdigital tools. Therefore low-level product design is digitized for stage 1. A description of the SIMMI 4.0maturity stages is given below.



1. Basic Digitization The company does not have any smart manufacturing capabilities. Processesare only partially digitized and information is scattered throughout the organization. Tools fordigital product design are available.

2. Cross-departmental digitization Information is no longer siloed but can be accessed within thedepartments. Enterprise systems are supporting both production and product development. Dataexchange is not yet automatized.

3. Vertical and Horizontal digitization Industry 4.0 requirements are implemented enterprise-wide. Production and product development systems are integrated and information is exchangedautomatically. The company established the Service-Oriented principles. Services can be madeavailable throughout the company by cloud solutions.

4. Full digitization Company is integrated into a value network, integration is enterprise-wide,cross-corporate horizontal and vertical. Information is automatically passed through the productlifecycle and across the value chain.

5. Optimized Full digitization All industry 4.0 activities within the company and across companyborders are optimized. New business models are created based on the collaborations in the valuenetwork.

Figure 5.3: Smart Manufacturing Framework

As the framework is now a 3D cube it is possible to rotate it to change the positioning of the dimensions,for example, swap the maturity and hierarchy dimension. It is also possible to make one of the currentexplicit dimensions implicit and therefore make the aspect dimension explicit. For the remainder ofthe report, the visualization as is presented in Figure 5.3 is used. This decision is made because itmaps directly to the maturity models shown in Figure 5.4. The Smart Manufacturing Framework isthe main artifact of this research and contains itself, several models. These models form the referencearchitecture. By cutting the reference model in vertical slices reveals the reference architecture, showingthe individual maturity stages and the positioned elements. The hierarchy level is positioned on the leftside of the model, similarly to the SMF, and the product lifecycle is positioned on top. The maturitystage is shown on the right side of the model, also in line with the SMF. The until now explicit aspectdimensions are now revealed in separate models. Each maturity stage is designed using three aspectsof Truijens framework (Grefen, 2016b). This design leads to a model that shows the three aspects andan integrated model of the software and platform aspect. This combination provides an insight intothe system integration. An example of a complete maturity stage, showing the four models, is given inFigure 5.4. In the remainder of the document, the visualizations of the aspects will be referred to asmaturity models.

According to Fraser et al. (2002) a maturity model should preferably present six components, (1)stages,(2) stage descriptors, (3) summary of the stage, (4) a number of dimensions, (5) a number of elementsin the dimensions, (6) and an activity description for each stage. The five stages and descriptors arealready shown in Figure 5.3. The number of stages, as well as the stage descriptors, are kept exactlythe same as in the SIMMI 4.0 model as a basis for the reference model. The descriptive summaries areadapted and provided earlier in this section.



(a) Process Aspect (b) Software Aspect

(c) Platform Aspect (d) Integrated Software Platform Aspect

Figure 5.4: Maturity Stage 1

The number of dimensions, which are in this case the aspects, and an example of elements within thedimension are shown in the figure above, Figure 5.4. The elements are adapted for each aspect, suchthat they represent an accurate viewpoint. This means overall functionalities in the process aspect, thatare specified using information systems in the software aspect and middleware is used in the platformaspect. All these elements have to be positioned according to their hierarchy level, lifecycle phase, andmaturity stage. This model contains 5 hierarchy levels, 3 lifecycle phases, 5 maturity stages and 3 aspectdimensions, which leads to 5x3x5x3=225 cells. This means that there are at least 225 design decisionsmade during the design of this reference model, as each cell can contain either 0, 1 or n elements. Itis difficult to provide a tight argumentation for each of these design decisions. In some cases the posi-tioning of elements can be argued using literature, for example when they are included in a standard.Nevertheless, it can be the case that literature does not provide a single answer or that some of theelements are not yet discussed in research. Then positioning is based on expert knowledge or an owninterpretation from existing use-cases. To ensure the readability of the report, only the design approachof stage 1 is elaborated upon in the next paragraph. The remainder of the stages is summarized in Table5.1. In case you, as a reader, are interested in the decisions that are made for each maturity model, thevisualizations of these models can be found in Appendix G, together with the design choices that arewritten down in bullet points.



5.4.1 Elaboration Maturity Level 1

Maturity Level 1 is described as basic digitization, with no smart manufacturing capabilities available.Only limited digital support for development processes is implemented. The company consists of dataislands and there is no or limited digital interaction between the departments. In case enterprise systemsare available they only support functionalities within their own department. Data integrity is not ensured.

Process AspectThe process aspect shows the overall functionalities that are available within the organization and caninvolve multiple hierarchy levels or lifecycle phases. Due to the limited support for product informationis scattered across the organization (Batenburg, Helms & Versendaal, 2006). For the development phase,this means that information on mechanical components is stored separately from information on electricalcomponents and integration only happens when the different designers decide that components must becombined. The functionality decentralized product data management is positioned on the production linelevel, as the components should integrate on this level, but are still siloed. According to Leyh, Bley,Schaffer and Forstenhausler (2016), there is no digital support for product development, which suggeststhat software tools for design are absent. However, based on expert knowledge this is an assumption thatdoes not match with reality in industries. For this reason, the process of product design is supporteddigitally. Hence, the functionality siloed digital product design is positioned on a work cell level wherethe design of components takes place. The positioning is based on the defined relations between thehierarchy levels and lifecycle phases presented in Figure 4.11.

It is assumed that at stage 1 enterprises possess machines and robots that execute manufacturing activ-ities. The manufacturing equipment available on the shop floor contains standard machine software.This enables partially digitized production management for a production line and single machine controlon a lower level. The functionalities of the well-known manufacturing pyramid are used to describe thesite level. Production is planned on a site level and business resources are planned on enterprise level(American National Standard, 2005). Based on the ISA-95 standard for manufacturing operations canbe defined, production operations, quality operations, maintenance operations and inventory operations(American National Standard, 2005). These are activities for production planning and are thus posi-tioned on a site level. The functionality for resource management, which is more a business functionality,is positioned on a site level. As the company still consists of data islands these functionalities all ex-ist individually. This means that a certain group is responsible for inventory operations and anothergroup is working op production operations. All schedules and plans that these functionalities require areshared on paper with shop floor workers, such that the processes can be executed. Note that there is nointegration between the various areas within the company.

In maturity level 1 the company is considering mainly its own operations and does not look for cooperationwith customers. This is shown by the fact that the company is still focusing on goods and is not service-oriented yet (Leyh, Schaffer, Bley & Forstenhausler, 2016). The relation with the customer is mainlyfocused on selling the product, so limited data and information are gathered. For that reason, thefunctionality for support towards the customer is ad-hoc production support and only happens when thecustomer requests it.

Software AspectAccording to the functionalities the product development is already digitally supported. Therefore,software tools are available for the design of products. A product is not simply designed using a singletool. It exists of different components that all have different specifications and thus requires a specificsoftware tool. These tools are often split into three different categories: Computer-Aided Design formechanical components(M-CAD), E-CAD is used for electrical components and for the software part aSoftware Development System (SWD) is used. This distinction and placement are verified with expertknowledge within Atos and the CAD system is also visualized in the Smart Manufacturing Ecosystem (Lu,Frechette & Morris, 2016). The design tools are positioned on a work cell level as they focus on componentdesign. The component data is managed on a production line level in databases. As information is stillsiloed within the departments, each system is linked to its own database. A developer can save its designprogress in a database and work further in the morning, but information on requirements for compatibleparts is unavailable.

On the work cell level machines are equipped with a Programmable Logic Controller (PLC). A PLC isa remote terminal unit, that due to the programming capabilities has established a more sophisticated



manner of monitoring and control (Endi, Elhalwagy et al., 2010). Equipping machines with a PLC is acommon practice in current industrial systems and provides real-time operations, based on its connectionwith sensors and actuators and the higher level SCADA system (Tomlein & Grønbæk, 2017). A SCADAsystem, which stands for Supervisory Control and Data Acquisition is, similarly to the PLC, widely usedfor industrial processes. The SCADA system is solely a software system, that interfaces with hardwarevia a PLC (Daneels & Salter, 1999). According to the ISA-95 manufacturing pyramid supervising andcontrol is a level 2 functionality, which indicates that it should be placed on the level of a production line.One of the main functionalities of SCADA is logging, which can be thought of as a medium-term datastoring. This data can be used by information systems on a higher level, that are responsible for the sitelevel functionalities. In this stage data is still siloed so for each of the functionalities at a site level, thereis an individual information system that executes it. The Manufacturing Execution System takes careof the actual scheduling of operations, the warehouse management system (WHMS) keeps track of theinventory levels of the various components and products, production quality is ensured by the QualityManagement System (QMS) and lastly maintenance of machines is scheduled and kept track of usingthe Maintenance Management System (MMS). According to Erasmus et al. (2018), these systems are alllevel 3 functionalities and thus are placed on top of the SCADA system. The resource planning of theentire enterprise is executed by an Enterprise Resource Planning system and is therefore placed on anenterprise level. All these elements together form the manufacturing pyramid (Lu, Frechette & Morris,2016).

There is not much digital support for when the product is in use and to provide services. Customer datais kept in an excel file and no proper Customer Database (CDB) is established yet.

Platform AspectNot many platforms are needed within the first maturity stage. Systems and information are stillscattered across the company and interoperability between systems is rare. In the development phase,each design system stores the files in a siloed database. This means that there is one-to-one modulecoupling between the systems, so the interoperability pattern between the systems is direct invocation(Grefen, 2016b). A Fieldbus enables the connection between SCADA and PLC. A field bus is an umbrellaterm for standards such as MODBUS and TCP/IP (Endi, Elhalwagy et al., 2010). This connection canbe either hardwired, point to point, or via radio.



5.4.2 Summary of Maturity Stages

Table 5.1 includes a summary of the elements that are relevant for each maturity stage. The table canbe seen as a delta matrix as only the elements that changed or are new are considered in the next stage,in other words an incremental description is provided. This provides an overview of which activities orimplementations are necessary to reach the new maturity stage. The columns represent the three aspectsas for each of the aspect different activities might be necessary to reach the next stage. The same holdsfor the various lifecycle phases, once the focus of a company lays with one of the phases it is possible toonly focus on the elements that are important for that phase. Lastly, a short summary is given for eachof the stages as a whole.

Table 5.1: Summary Maturity Stages

Process Aspect Software Aspect Platform Aspect

1Company is not engaged in Industry 4.0 activities and information is siloed even within departments.Product development and production are only partly digitally supported.

Bas

icD

igit

izati

on Development Siloed Digital Product De-velopment

Product Design support,E-CAD,M-CAD,SWD

Direct Invocation

Production Decentral ProductionManagement

Machines are connectedby PLC and controlledby SCADA. OperationsManagement systemsare siloed, MES, QMS,WHMS, MMS, and par-tially available. ERPsystem on EnterpriseLevel

Fieldbus between workcell and production line

Usage/Service Ad-Hoc Services Excel as customer data-base (CDB)

N/A

2

Individual departments are now digitally supported. Industry 4.0 activities are present across theenterprise. However, data exchange is not automated across departments, thus activities are stilldecentralized.

Cro

ss-D

epar

tmen

tal

Dig

itiz

ati

on

Development Product data manage-ment

Digital PDM support, alldesign elements from thework cell are now stored insystems engineering soft-ware. On a higher level,information is stored in aPDM and PLM

Shared Repository

Production Coordinated ProductionSupport

Some machines areequipped with IoT soft-ware and sensors andactuators. IntegratedManufacturing Oper-ations ManagementSystem (MOMS) and or-chestrated manufacturingoperations, using a busi-ness process managementsystem (BPMS)

Enterprise Service Bus(ESB) on site level toconnect MOMS withlower hierarchy levels.BPMS is used to or-chestrate manufacturingoperations.

Usage/Service Decentralized support Customer Database Sys-tem (CDB)

N/A



3

The smart factory is established, the full manufacturing pyramid operates autonomously.Enterprise-wide digitization of product development, production, and services. EnterpriseService Bus and Cloud services are established to connect the various enterprisesystems and serve the entire company.

Hor

izonta

lan

dV

erti

cal

Dig

itiz

atio

n

Development Digital Product Twin Service Lifecycle Manage-ment(SLM) available tobuild a service-orientedorganization and managethe services that are avail-able.

Enterprise Service Bus tointegrate support productdata management sys-tems. The ESB is alsoused to integrate PLM,SLM, and ERP on anenterprise level to supportboth development andproduction. These sys-tems can be in the cloud,private or public, but arealways integrated by anenterprise bus.

Production Smart Factory PLM and SLM also in-tegrated on an enterpriselevel within the produc-tion phase

IoT Communication Layerto process real-time databetween work cell and sitelevel, combined with aSmart Factory Informa-tion Service Bus (SIBUS).SIBUS integrates both in-formation systems withshopfloor hardware andIoT components. TheESB is also used to integ-rate PLM, SLM, and ERPon an enterprise level tosupport both developmentand production. Thesesystems can be in thecloud, private or public,but are always integratedby an enterprise bus.

Usage/Service Decentralized CustomerSupport

CDB N/A

4

Companies digitization stretches beyond corporate borders. The role of the customer and the fastinnovation cycle becomes important for improved development and autonomous production. Nextto the product and production twin, there is also a customer twin. Information in enterprise systemsis standardized and accessible throughout the lifecycle, using cloud services. Suppliersand customers are all integrated into a collaborative value network.

Fu

llD

igit

izat

ion

Development Integrated Product Life-cycle support

PLM and SLM transfer tothe network level to ensurethat development activit-ies are accessible in thevalue network.

Both information systemson a network level can bein the cloud and are con-nected via an ESB. Thecloud helps to standard-ize, such that data canbe used across the supplychain.



Production Optimized Smart Factory Supply Chain Manage-ment (SCM) is added tosupport the productionbased on information fromsuppliers and customers,which helps to realize theoptimized smart factory.For production PLM andSLM are on a networklevel to enhance the col-laborative value network.

The information systemson a network and site levelcan be in the cloud, es-pecially on network levelthis is a good solution forstandardization. An ESBserves as middleware tointegrate the informationsystems on network andsite level.

Usage/Service Integrated Service Life-cycle Management

IoT Software embedded inthe product in use. CRMsystem that keeps trackof the customer inform-ation. The SLM andPLM stretch towards theUsage/Service to provideservices to the customerand keep track of exactproduct data.

The IoT communicationlayer also stretches to-wards this phase to en-sure real-time data fromthe product in use is cap-tured. The CRM is con-nected to the other enter-prise systems by an ESB,in the cloud or local datacenter.

5

The company is an example for others that want to develop smart manufacturing. The integratedenterprise systems and collaborative value network drive for new business models and services tooffer customer. Internal and external processes are optimized.

Op

tim

ized

Fu

llD

igit

izat

ion

Development Optimized Digital Twinand Product Lifecycle

Improved use of availabledata from the enterprisesystems. Using data ana-lytics and simulations torespond quickly to chan-ging customer demands.

All enterprise systems arein the cloud to ensure se-curity, availability and thestandardization of data.Interoperability is ensuredby the ESB.

Production Optimized Smart factoryand Smart Enterprise

Real-time analysis basedon shop-floor data anddata from the collaborat-ive value network. Cus-tomer orders can be pro-duced autonomously, us-ing flexible manufacturingsystems.

All Enterprise systems arein the cloud to ensure se-curity, availability and thestandardization of data.Interoperability is ensuredby the ESB.

Usage/Service Connected Customer Data from the productin use is processed andused to optimize the de-velopment and productionphase. Sales configuratoris accessible for the cus-tomer and generates or-ders that are fulfilled auto-matically using the pro-duction systems and sup-ply chain. Predictivemaintenance is an integ-rated service for the client.

The connectivity of thereal-time product dataplays an important roleto optimize the entireenterprise.



The table above can be used to determine which activities should be performed to reach the next stage, asit provides an overview of the deviations between the sequential stages. While discussing the frameworkwith experts at Atos and also based on the results of the implementation, some of these deviationswere considered to be a bigger game changers than others. Therefore, a couple of these activities arehighlighted to emphasize their importance.

To reach maturity stage 2 it is important that both the product development and production are coordin-ated. For product development, the implementation of a PLM system is key. According to Batenburget al. (2006), implementation of a PLM system is at first a data management problem that is dealtwith on a departmental level. This matches with the adapted description of maturity stage 2. Theactivities still happen on a departmental level and not yet across departments. Furthermore, it is alsocommercially more attractive to split the activities of product development and production engineeringon level 2 and integrate these processes on level 3, as this often resembles the real world scenario. Forthe coordinated production support, the implementation of the Manufacturing Operations ManagementSystem is important. Often a company mainly considers a Manufacturing Execution System that inits broad definition suggests it contains all the functionalities of a MOMS (i.e. production, inventory,maintenance, and quality operations) (Erasmus et al., 2018). A visualization including the MOMS ischosen to explicitly show the integration of those functionalities into one operating system for productionis an important milestone and highlights an aspect that is not always considered.

The previous paragraph also indicates an important and valuable change to reach level 3, namely theintegration of the functionalities product development and product engineering. The ISA-95 standardmainly suggests vertical collaboration from the Enterprise Resource Planning system to production.The ERP system takes care of production planning and scheduling, whereas product-related data forproduction management are recorded in the MES (Khedher, Henry & Bouras, 2011). However, withthe evolved PLM systems the same information can be stored in multiple systems. Therefore, the ERPsystem does not have to serve as an interaction between PLM and MES, but it is better to have adirect connection between the three systems. This way detailed product data and even detailed workinstructions can be sent directly from the PLM to the MES and only higher level production schedulingis executed by the ERP system, Figure 5.5. The data exchange between these platforms also requiresthe implementation of a platform to ensure the interoperability of these systems. As the enterprise isalso becoming service-oriented the Enterprise Service Bus is a logical solution. This common middlewareallows for flexible m-to-n module coupling (Grefen, 2016b). To support the decision for ESB within thefourth industrial revolution an example of an I4.0 application is used. In this example of an Industry 4.0application, the enterprise service bus is used as middleware between the ERP system and a databasefor static data, as well as a lower level SCADA element (Marseu, Kolberg & Weyer, 2017).

Figure 5.5: Integration PLM, MES and ERP, adapted from Khedher, Henry and Bouras (2011)



The last activity that also leads to an important change within and beyond the enterprise is the estab-lishment of a collaborative value network at maturity stage 4. Using the Internet of Things to gatherinformation from the product in use and thereby connecting the customer is an important step for im-proving the product lifecycle. A closed-loop lifecycle ensures that within each lifecycle phase the relevantdata is available (Kiritsis, 2011). For example, the designers know exactly which components causedtroubles during production, based on the shop floor information. Maintenance experts know, based onpredictive maintenance analysis, when service must be provided and are assisted during their work usingAugmented Reality. The connection of the customer and the supplier leads to a fast innovation cyclebased on accurate data, such that a quick and flexible response to the changing environment is possible.Figure 5.6 shows how the data from the physical world is used to create three digital twins in the virtualworld, the connection between OT and IT. The digital twin is a virtual model of a complex product andallows for probabilistic simulations to mirror the life of its physical twin (Tao et al., 2018). It consistsof a physical and virtual component and data connecting those two. The virtual model can be used fordata analytics to improve the lifecycle, but can also help to deploy augmented and virtual reality.

Figure 5.6: Closed Product Lifecycle, taken from Atos documents

The Smart Manufacturing Framework, as it is visualized in Figure 5.3 can be used as a tool to supportcompanies in the development of smart manufacturing practices. The individual maturity stages show aroadmap from a very early stage of implementation until an optimized collaborative smart manufacturingnetwork. The functionalities and elements displayed in the models help to create an idea of the necessitiesto engage in smart manufacturing practices. The framework does not claim to be complete but doesshow some of the key elements for the development of smart manufacturing. How the framework can beput into use is presented in the next chapter.


Chapter 6

Framework Implementation

The implementation of the artifact is the fourth step in the problem-solving cycle of van Aken et al. (2012)and is used to measure the relevance of the framework. The implementation of the framework takes placein two real-world scenarios, in other words, a case study is conducted at two different companies. Theβ-version, the smart manufacturing framework is implemented, as it was concluded that the α-versionof the framework was not suitable for implementation. First, the implementation approach is discussed.Second, the meta-results of the implementation are presented. Lastly, a short conclusion is provided onthe overall results of the implementation

6.1 Implementation Approach

Figure 6.1: Implementation Activities

The implementation of the framework is divided into fourmain steps, as shown in Figure 6.1. First, the current ma-turity stage is assessed, to create a proper starting point.Second, the desired maturity stage is determined, such that aroad between the two can be built. However, before that roadcan be created, the differences between the two stages mustbe analyzed. Lastly, the activities that are necessary to reachthe desired maturity level are selected and described. Thisprovides the final result of the implementation, the smartmanufacturing roadmap. Creating the roadmap for the par-ticipant is not the only goal of the case study. A secondgoal is to validate the usability of the framework when itis applied in industry. Additionally, it is verified if the ma-turity stages and the defining elements are chosen correctly.Therefore, multiple methods are used in the first and secondstep of the implementation to determine if these methodsprovide a similar outcome. The usage of the various meth-ods is based on the assessment of the SIMMI 4.0 maturitymodel, where they conducted a self-assessment based on ahigh-level overview as well as a more detailed questionnaire(Leyh et al., 2017). The implementation steps and different methods are described in this section.

Step 1: Assess current maturity level

The goal of the first step is to determine the current maturity level of the enterprise that serves as astarting point for the transformation. For the purpose of validation, three different approaches are used.The first approach is a high-level assessment, only based on the main functionalities of each maturitystage. The second approach is a detailed questionnaire, based on the practices within the four key smartmanufacturing aspects. Lastly, the current situation of the organization is compared with the four ma-turity models of the maturity stage that resulted from the questionnaire.


CHAPTER 6. FRAMEWORK IMPLEMENTATION

High-level Assessment based on Maturity Model

The high-level assessment is based on an overall description of each lifecycle phase per maturity stage.This description can be seen as a summary of Table 5.1, where the aspect dimensions are no longersplit. The table is already too detailed for an interviewee to easily determine the current maturity stage.Therefore, the high-level overview of the maturity models is visualized and shown in Figure 6.2, to createa first insight into the companies maturity level. The descriptions in the maturity table are based onfunctional elements from the actual maturity models.

The interviewee is asked to position the company, for each of the life-cycle phases. When the intervieweeis in doubt between two stages, it is decided to take the lower maturity stage. It is often the case that someactivities in the higher maturity stage are already executed, but full establishment is not yet reached.Therefore, the stage that is fully established is marked as the current situation. The positioning of thecurrent IT landscape based on the lifecycle phase can lead to different maturity stages. For example,in the development phase the company can already establish a digital product twin, but at the sametime provide ad-hoc services. This results in a scattered IT landscape based on the maturity table andidentifies a gap within the current state.

Figure 6.2: Maturity table for the life-cycle phases

Detailed questionnaire

The second method is a detailed questionnaire based on the four aspects of smart manufacturing: (1)Vertical Integration, (2) Horizontal Integration, (3) End-to-End Integration, (4) Technological support.The questionnaire is used in a face-to-face interview. There are three perspectives on an interviewmethod: (1) Neopositivism, the interview is used as a tool to collect data, (2) Romanticism, the interviewis a human encounter to reveal experiences, (3) Localism, puts interviews into a social context and studiesan empirical situation (Qu & Dumay, 2011). In this situation, the interview is used to transfer data andknowledge objectively. The interviewee tends to tell the truth about their current situation and theirIT landscape. A Neopositivism perspective suggests the use of either a structured or semi-structuredinterview. In a structured interview, the interviewer asks a set of questions that are pre-established andthe interviewee can only answer based on a limited number of response categories. Strictly, it does notallow for any deviations from the script. A semi-structured interview also uses a pre-established set ofquestions but these questions less strict and narrow. The questions are divided into themes and useprobes to stimulate more elaborate responses. This approach is mainly based on guiding the interviewto stay on the topic as planned.

The approach that is used for the implementation is semi-structured. The aim is to receive elaboratedresponses because only a limited amount of interviews can be conducted. The questions serve as aguideline to discuss the four key aspects of smart manufacturing as well as the relevance for each lifecyclephase. The semi-structured approach leaves some flexibility during the interview, to ignore questionsthat are irrelevant and ask follow-up questions where necessary. For example, when a company onlyexecutes the final assembly of a product, the automation of the factory might be irrelevant. In thatcase, questions related to IoT on the shop floor and system integration in the production phase can be



omitted.

The questions are based on the questionnaire used for the assessment of the SIMMI 4.0 maturity model ofLeyh et al. (2017) and the questionnaire for the maturity and readiness model for industry 4.0 strategyof Akdil, Ustundag and Cevikcan (2018). The questionnaires are considered to be a valid basis asboth maturity models were assessed using these questionnaires. The complete questionnaire consistsof 30 questions and sub-questions and can be found in Appendix H. Based on the answers the currentmaturity stage of the company can be determined. The example questionnaires use a point system todetermine the maturity stage. Their approach leans towards a structured interview and many of thequestions have a limited number of possible answers. For each of these answers, a number of points canbe scored. The rounded down average that results from the questionnaire, is the maturity stage that isdetermined. As the approach for this interview is semi-structured, it is difficult to use a point system.Due to the fact that there are no given answer possibilities, it is hard to give a point to a certain answer.The open answers are used to determine which functionalities, systems and platforms are in place in thecurrent IT landscape. These can be mapped to the same elements in the maturity models belonging toa maturity stage. In case all elements or almost all elements are in place for a certain stage, the currentmaturity stage is determined.

Model-based verification

The outcome of the questionnaire and high-level assessment is compared with the maturity models.Both the integrated aspect, including the software and platform aspect, and the process aspect are usedto verify the results. The interview is asked if the models do represent the current processes and ITlandscape that is present in the company. In some situations, it might be the case that certain elementsthat are displayed in the models are missing in the current IT landscape of the company. This meansthat there exists a gap in the current maturity level that should be addressed.

Step 2 Determine Industry 4.0 vision

The goal of the second step is to determine the desired state of the organization, based on the maturitystages of smart manufacturing. Again multiple approaches are used to determine the TO-BE situationas was used in the AS-IS situation.

High-level desired maturity stage based on maturity model

Similar to the approach for the current situation, the maturity model, Figure 6.2, is used to create afirst insight into the desired maturity level. This provides again the possibility to position the companyat different maturity stages based on the various life-cycle steps.

Ranking of smart manufacturing key capabilities

The second method provides more insight into the goals of the company regarding the fourth industrialrevolution. Based on the four key capabilities of smart manufacturing as determined by Lu, Frechetteand Morris (2016), it is possible to determine what is most relevant to a company. The key capabilitiesare (1) Productivity, high throughput and reduced labor hours, (2)Agility, quickly respond to changingdemands, (3) Quality, customer service, innovativeness and quality of the product, (4)Sustainability,product durability and recyclability. Each of these capabilities corresponds to aspects of the smart man-ufacturing ecosystem, such as Supply Chain Management and Design for Manufacturing and Assembly.An overview of the aspects and the corresponding capabilities is provided in Table H.1, which can befound in Appendix H. Based on the table introduced by Lu, Frechette and Morris (2016), a mappingto the functionalities that are represented in the SMF is made, shown in Table 6.1. The table works asfollows. Once productivity is ranked as capability number one it is verified by asking questions relatedto the functionalities corresponding to productivity. An example question can be ”is a bi-directionalautomated flow in the manufacturing pyramid necessary?”. This corresponds to a level 3 maturity, theestablishment of a smart factory.



Table 6.1: Smart manufacturing capabilities linked to maturity model stages

Productivity Agility Quality Sustainability

2 Design forManufacturing

3 Product Twin 2 Manufacturing Pyramid 2 Manufacturing Pyramid

3 Smart Factory 4 Fast Innovation Cycle3 Continuous ProcessImprovement

3 Smart Factory

4 Smart ConnectedFactories

4 Flexible ManufacturingSystem

4 Fast Innovation Cycle 4 Integrated PLM

5 Optimized SmartFactories

4 Integrated SCM 5 Mass Customization

Model-based verification

Once the desired maturity stage is established using the previous two methods, it is verified using themodels presenting the process aspect and integrated aspect. Together with the interviewee the processesand the systems are compared with the expectations and the vision, to evaluate the necessity of theelements that are displayed in the models. It can happen that the interviewee concludes that some ofthe elements in the maturity model are not relevant, maybe they do not want to track the product inuse. In that case, the elements that represent monitoring a product in use are left out of scope. It canalso be the case that the maturity for those aspects should be positioned on a lower stage than the stagethat was found using the previous two methods.

Step 3 Gap Analysis

In order to construct the roadmap, a gap analysis is performed between the two maturity stages. In caseanalysis must be made between stages 1 and 3 or 2 and 5, the intermediate stages are also considered. Anexample of a gap analysis of the platform aspect between maturity level 2 and 3 is shown in Figure 6.3.First, it must be noticed that the Enterprise Service Bus captures two lifecycle phases and is extendedto the enterprise level. This ensures interoperability between the development and production phase aswell as vertical integration of the systems. Furthermore, enterprise information systems can be in a cloudapplication. As the cloud is not an actual platform it is here visualized using an annotation, based onthe example of Grefen, Vanderfeesten and Boultadakis (2016a). To establish the smart factory, the IoTcommunication layer is necessary to connect the information systems to the shop floor. Additionally, adedicated smart factory BUS for connectivity between hardware and software, SIBUS, is implemented.

Figure 6.3: Gap Analysis between maturity level 2 and 3

Comparing the models provides information on the systems that must be installed, the need for platformsand the level of system integration. However, it also implies other activities that are not directly visiblein the maturity models. An IoT communication layer implies that data from the shop floor is gathered



and processed on a higher hierarchy level. This means sensors are necessary to collect the data, datamodels are necessary to run data analytics and it must be clear which data must be gathered. Theseelements are implicit in the models but do play an important role in the successful establishment of thematurity stages. The activities that are necessary to bridge the gap between the AS-IS situation andTO-BE situation, both implicit and explicit, are put into a logical order that forms a roadmap. Theroadmap can be executed such that the desired maturity level is reached and full value can be extracted.

Step 4 Create a Roadmap

The roadmap is the result of the implementation of the Smart Manufacturing Framework. It includesactivities and actions that must be taken to reach the desired maturity level. These actions can bederived from the various models and constructed into a sequence of steps. The employees at Atos thatwill use the framework can provide an in-depth roadmap that includes an overview of data mitigationand integration as well as software packages and functionalities of specific vendors. However, for thissituation to give a first introduction to the application of the framework, the roadmap is constructed ona higher abstraction level. In the next section two summaries of roadmaps are given, to create a generalfeeling of the final result.

6.2 Implementation Results

As was mentioned in the introduction of this chapter, the above described approach is applied to tworeal-life scenarios. Both companies agreed with the fact that the results are used for this report and thuswill be available for the public. However, the results are anonymized, such that none of the answers canbe related to a specific company. The interview is not transcribed, but audio recordings of the interviewhave been made and are accessible upon request (Participant1, 2019; Participant2, 2019). Note thatthese audio recordings are also anonymized. A short description of both case-studies is given below.

The first company is a start-up in the automotive industry and falls in the range of 50-99 employees. Asa startup, it has no restrictions due to legacy systems and is at the start of creating their IT landscape,a greenfield starting point. The company is focused on smart manufacturing and wants to apply thesepractices to their company. The interviewee is mainly involved in the development phase but also hasthe knowledge and vision for the other phases of the lifecycle. Furthermore, the interviewee is aware ofthe current IT landscape as he works within the IT department.

The second company is a well established Original Equipment Manufacturer (OEM) in the food pro-cessing industry. As OEM the main production activities focus on the assembly of the sub-systems tocreate a final product and the production of small components to execute the assemblage. This interna-tional company has over 5000 employees and is far more advanced in establishing their vision for smartmanufacturing practices. The interviewee mainly focuses on software development for the product in useand is therefore also at the development site. However, due to a great level of experience, the intervieweehas a good insight into the current IT landscape.

First, the main results of each activity are discussed for case-study 1 and thereafter for case-study 2. Thesection is concluded by analyzing the main results and the implication of these results on the framework.

6.2.1 Case-Study 1

The overall outcome of the assessment of the current and desired maturity stage is provided in Figure6.4. The blue cells show the current maturity stage and the green cell show the desired maturity stage.It can be seen that the current IT landscape is not positioned on a single stage. The production phase isstill on a level 1 maturity, whereas the other lifecycle phases are already evolved to stage 2. The desiredmaturity stage is set on maturity level 4. The optimization of the entire ecosystem was at this momentnot considered as the final situation, as the main goal is now the implementation of the right systems.



Figure 6.4: Main results Case 1

Step 1

First, the interviewee is asked to position the company based on the maturity table, the high-levelassessment. The company manages its data using a PLM system, but it is still departmental. Thereforedevelopment is positioned on maturity stage 2. The company is not yet producing their product, whichmakes the placement of the production phase hard. A couple of test products were produced in acoordinated manner. The production phase is positioned at the very beginning of stage 2. Currently,the services that are offered are still decentralized. Customer data is kept, but this is mainly for salespurposes. Lastly, the organization of the company is departmental. This results in an overall high-levelview of maturity stage 2, with a side note for production that this is positioned at the very beginning.

The results of the detailed questionnaire are quite similar. To support the vertical integration withindevelopment CAD tools, a PDM and a connection to a PLM are available. The ERP system is imple-mented for resource planning, which resources are needed at which time. However, there are no systemsthat support the production environment except for the ERP system, so no vertical integration for theproduction environment. There is no CRM system to support the connection with the customer, limitedhorizontal integration. The interoperability between PLM and ERP is offline. The suppliers are nowmanaged in the ERP system, so no individual SCM system. For end-to-end integration, there is alreadya link between production and development, as the manufacturing processes are available in the PLMsystem. This is mainly due to a lack of a MOMS. Lastly, the PLM system is already a cloud-solution.However, it is only accessible within the enterprise. Using these outcomes results in almost the samematurity stages as the high-level assessment. All elements for maturity stage 2 are present, except forthe elements in the production phase. Therefore, the production phase is positioned on maturity level 1and the remaining phases are placed on level 2.

The maturity models were used to verify the elements depicted in these models. Based on the modelsfor maturity stage 1 and 2, it was indeed possible to map the functionalities and software systems to thecurrent situation. However, it was also concluded that the cells in the production phase on the two lowestlevel were not relevant for this company. Automation of machines is not applicable in this situation. Thecompany uses a cloud solution for PLM, but the cloud is not included in the stage 2 maturity model.However, as the cloud is not a platform but merely an annotation it is decided that this is not a mistakein the model. Especially, because the PLM is not integrated with one of the other enterprise systemsusing an enterprise bus.

The evaluation of the AS-IS situation led to the following visualization, shown in Figure 6.5. The greyareas are out of scope for this situation. The red circles show the focus areas that are still lacking forthat maturity stage.



Figure 6.5: AS-IS situation case study 1

Step 2

During the questionnaire in step 1, there were already answers that helped to determine the desiredsituation. However, for the consistency of the implementation approach, these answers are not takeninto account. It is important to know upfront that this company is restricted by many regulations thatthey have to comply with. For this reason, the desired maturity stage was very clear ”we have to moveto stage 4”.

Traceability of the product is extremely important from source to customer, therefore there should beintegrated product lifecycle support. Although a Smart Factory with autonomous shop floor is notdirectly necessary, it is preferable to have orders automatically transformed into work instructions, suchthat the shop floor employees can assemble the product. Similarly to the integrated product lifecycle, theintegrated service lifecycle management is necessary to support the product and provide customers withservices. Furthermore, the value network must be established such that product information is availableacross the supply chain.

The smart manufacturing capabilities were ranked as follows: (1) Quality, (2) Agility, (3) Sustainabilityand (4) Productivity. Based on the quality aspect product lifecycle management should be across theentire lifecycle. In case something is wrong with the product immediate improvements must be made.This complies with the Fast Innovation Cycle, established on level 4, which comes together with theintegrated product lifecycle if data is used properly. For agility, there are three functionalities that arelinked to stage 4. The Fast Innovation Cycle was already determined, but also integrated SCM androuting of the production are considered to be relevant aspects. The integrated PLM must be met forsustainability, level 4. Productivity is not considered important and a smart factory on level 3 wouldbe sufficient as was mentioned above. However, the integration with the network is considered relevantfor the company and therefore the TO-BE state is positioned on maturity stage 4. The missing aspectsare visualized in Figure 6.6. What is interesting is that the IoT software on the product in use wasconsidered to be out of scope, while the fast innovation cycle is a desired functionality. This had to dowith the fact that accurate information about the product on each component is desirable, but real-timeperformance is not. Hence, improvements and activities based on accurate product data is still a relevantfunctionality for this company.

Step 3 will not be discussed explicitly here, as the gaps are already represented in the visualizationsof step 1 and step 2. Furthermore, the gaps are also addressed during the roadmap design. The gapanalysis can be found in Appendix H.



Figure 6.6: TO-BE situation case study 1

Step 4

A summary of the roadmap just containing the main activities is provided here. A more elaborate versioncan be found in Appendix H. The roadmap is based on the results from step 1 and 2. First, the currentIT landscape needs to be brought to one maturity stage, the production phase must move from stage1, or maybe even stage 0, to 2. Thereafter, the whole IT landscape should move from stage 2 to 4,including some activities to establish stage 3. It might be the case that not all elements of stage 3 haveto be implemented before the company can move to stage 4.

• Activity 1 Implement a Manufacturing Operations Management System, this can be a MESsystem with additional functionalities.

• Activity 2 Integrate the PLM and the MES system.

• Activity 3 Implement the SLM system and establish a service-oriented organization.

• Activity 4 Establish the digital product and production twin, by building simulation models andcreating data analytics models.

• Activity 5 Make the PLM system accessible to the suppliers.

• Activity 6 Implement a SCM system.

• Activity 7 Implement an integrated CRM system.

The activities must be executed over a longer time period, as the data migration, implementation ofsystems and roll-out take time. Based on estimations of the customer it will take approximately 5 years.

6.2.2 Case-Study 2

The overall outcome of the assessment of the current and desired maturity stage is provided in Figure6.7. The blue cells show the current maturity stage and the green cell show the desired maturity stage.It can be seen that the current IT landscape is not yet at the same maturity stage, the production phaseis still on a level 2 maturity. The desired maturity stage is set on maturity level 4. Similarly to the firstcase-study, the first goal is to include all the relevant elements and optimization might happen in a laterstage but is not considered as relevant now.



Figure 6.7: Main results Case 2

Step 1

In this case study, the high-level assessment based on the maturity table was not that straightforward.The enterprise grew mainly due to acquisitions of other companies across the globe. Therefore, theassessment of the entire organization led to a very scattered view. Within the organization, not allsub-organizations are at the same maturity stage. A recently acquired smaller company can be atstage one, while a well-established firm is already in the transition towards stage 4. For this reason,the assessment was scoped to one of the sub-organizations. This organization is responsible for a highpercentage of the activities within the organization and is, therefore, a good focus area. Even though theassessment does no longer include the entire organization, it does provide a more concise insight. Notethat when mentioning the enterprise in the remainder of the case study, it means the sub-organizationunless explicitly stated otherwise.

Based on the maturity table, the interviewee positioned the current IT landscape at an overall stage 3maturity, vertical and horizontal integration. Only production is not on a level 3 maturity, autonomousproduction, which is also not an immediate need. As an OEM organization, the main production activityis the assembly of subsystems and sell the systems as a whole to the client. Often this does not includemachines, but mainly manpower. Some small components that are always needed are produced 24/7 onthe shop floor using machines. It might be beneficial to automate this process and only produce theseelements when necessary based on the orders.

The results of the questionnaire were rather similar to the answers provided above. Many of the enterprisesystems are already cloud-based, either in a public or private cloud and accessible enterprise-wide. ThePLM system operates across the production and development department and connects to the ERP andMES system. For this situation, there are multiple production facilities and the production occurs at aglobal enterprise level, so for the entire organization, not the sub-organizations. The company is workingtowards a flexible routing of production within these different production facilities. The MES system cangenerate production orders, such that the people on the shop floor can produce efficiently. Some workcells can produce autonomously but they are not integrated with any of the higher level informationsystems. Therefore connectivity in the full manufacturing pyramid is not established. As was mentionedabove it is questionable if this situation is necessary. First steps towards an integrated service lifecyclemanagement are taken, although this still happens in an offline manner. A CRM system including asales configurator is available. Furthermore, horizontal integration is supported by the SCM system.The SCM is thus positioned on an enterprise level. The enterprise is not service-oriented although itdoes provide the necessary services to their customers, their main purpose is still selling goods.

Based on the maturity models it could be determined that there are two elements missing in the currentassessed maturity stage, the service lifecycle management system and the fully integrated manufacturingpyramid. The latter is already discussed, but the first is a focus point for the enterprise. A SLM can helpto transform the company towards a more service-oriented organization. The assessment of the AS-ISsituation led to the visualization shown in Figure 6.8.



Figure 6.8: AS-IS situation case-study 2

Step 2

Based on the information in Table 6.1, the desired situation is positioned at stage 4, full digitization.This is mainly triggered by the desire to integrate the entire lifecycle, stretching towards the integratedcustomer, and an established value network with partners. Currently, information on the product inuse is captured in the PLM system. This information is static and offline and needs to be adaptedmanually based on reports. This is mainly product types or maintenance information. It would bebeneficial to record real-data from the product in use, such that on an enterprise level relevant analysiscan be performed. These analyses can be executed to improve product design, fast innovation cycle, orpredictive maintenance. Furthermore, scalability and standardization were of importance such that aglobal approach can be used. Therefore, a global PLM and ERP system is necessary such that orderscan be automatically distributed across the various production facilities, this leads to connected smartfactories. As supply chain and demand planning are decided upon globally it is important that thesystems are also standardized globally, to ensure agility, productivity, and quality. An integrated PLMin the cloud can help to provide this standardization and ensure the sustainability of the products. Lastly,there are opportunities to establish a collaborative value network that enables an autonomous supplychain. An autonomous supply chain responds to data aggregated from the work cell level. Based onchanges in the design, inventory or product status, relevant supply chain information can be generated.This can be an automated order or changes in spare parts. The integration also stretches towards thecustomers, such that product configurations are automatically processed. The red marks in Figure 6.9arethe focus areas for maturity level 4.

Step 3 will not be discussed explicitly here, as the gaps are already represented in the visualizationsof step 1 and step 2. Furthermore, the gaps are also addressed during the roadmap design. The gapanalysis can be found in Appendix H.



Figure 6.9: TO-BE situation case-study 2

Step 4

A summary of the roadmap containing the main activities is provided here. A more elaborate versioncan be found in Appendix H. The roadmap is based on the results from step 1 and 2. The companyneeds to move from a stage 3maturity towards a stage 4 maturity.

• Activity 1 Implement digital service lifecycle management.

• Activity 2 Establish the digital product and production twin, by building simulation models andcreating data analytics models.

• Activity 3 Standardize the PLM system and ERP system so they are globally accessible, for theentire organization.

• Activity 4 Develop Smart Products.

• Activity 5 Make the PLM system accessible to the suppliers.

• Activity 6 Implement a SCM system on a network level.

• Activity 7 Implement an integrated CRM system.

6.3 Conclusion on Implementation Results

The implementation of the framework was successful in both case studies. All the steps of the approachcould be executed and both times it led to the creation of the roadmap, the final result of the frameworkimplementation. Both participants mentioned that the result was valuable. For company 1 it providedinsights into the elements that are still missing in both their current and desired situation. For company2 it mainly confirmed their vision and the correctness of their approach towards the future. Bothcompanies vary a lot in their current maturity state. Company 1 is a start-up and some processes arestill at maturity stage 1, while company 2 is already moving towards maturity stage 4. For this reason,they are two interesting case-studies as two extremes are analyzed that both recognize their own valuein the application of the framework.



It must be noted that the second case-study had to be adjusted to make the implementation feas-ible. Therefore, the assessment could not be executed for the entire organization, but only for a sub-organization. Nevertheless, the result of the assessment was still valuable. The current situation asit was assessed, was already at stage 3 and is also the level that the outside world expects from thiscompany. However, when acquiring a new and smaller business, it is likely that their maturity is notanywhere near stage 3. The framework can then be used to assess the maturity of this sub-organizationand provide a roadmap to bring it to the same stage as the main organization. From that stage, all thesub-organizations can move on towards the set desired state of the whole organization. This way theframework can be used as an internal tool for smart manufacturing development.

The framework allows leaving aspects out of scope when they are not considered relevant. In case-study1 the lower hierarchy levels, work-cell, and production line, in the development and usage/service phasewere considered irrelevant for the company. Nevertheless, the assessment for the AS-IS and TO-BEsituation is still relevant to the remaining aspects.

For a company with a greenfield starting point, the overview the framework offered was deemed veryvaluable. During the implementation of the framework, some key points that are quite important tosupport the development of the company were recognized. The approach also led to important insightsfor Atos, as it recognized several opportunities where they could support the company. For example, itwas pointed out that the company still needs a MOMS and Atos can help to define functionalities of thissystem.

Both participants asked questions on what a certain step would provide in terms of business value forthe company. According to Participant 2, the framework is very pragmatic and shows the key aspectsof both a high-level and an applicable level. However, it would have contributed to the approach ifbusiness cases could be connected to a certain maturity stage. For example, if a successful use-case canbe shown in which a smart factory was established implemented a smart factory, the benefits of thisimplementation can be shown. This makes it easier for a company to visualize a certain scenario and toidentify if implementing certain systems would be beneficial for the company.

It is difficult to conclude anything on the general applicability of the framework, as the implementationapproach was only executed twice. It can cautiously be concluded that the framework approach works,as the result was successful twice and it is fair to say that it was not just beginners luck the first time.What also argues in favor of the framework is that the cases were very different and that the generalapproach was successful for both. Even though the value created for the companies was different, bothcompanies agreed upon the importance of the framework.


Chapter 7

Evaluation

In this section, the Smart Manufacturing Framework is evaluated based on its relevance. The relevanceof the SMF is directly evaluated by the participants after the implementation of the framework. Theevaluation approach that is used is based on the criteria of Shrivastava (1987). According to Shrivast-ava (1987), the practical usefulness of the solution design, the smart manufacturing framework, canbe assessed by five criteria: (1) Meaningfulness, (2) Goal relevance, (3) Operational Validity, (4) In-novativeness, and (5) Cost of implementation. An explanation of these criteria is presented in Table7.1

Table 7.1: Relevance criteria adapted from Shrivastava (1987)

Criteria Explanation

MeaningfulnessThe framework is meaningful, easy to understand and enhances problemsrelevant for a discrete manufacturer

Goal relevance The framework adheres goals that are relevant for a discrete manufacturer

Operational Validity The framework provides clear actions

InnovativenessThe framework surpasses common solutions and provides new practicalinsights

Cost of implementation The framework shows feasible solutions in terms of costs and time.

The participants were asked to answer several open questions based on the five criteria. Furthermore, theevaluation also included a Likert-scale questionnaire ranking from 1, not at all satisfied, i.e. this criterionwas not met at all, to 5 completely satisfied, i.e. the framework discussed this criterion completely. Eachof the elements that must be ranked is related to one of the five criteria. Note, that the criteria wereexplained to the participants and the open questions also served to indicate the meaning of the criteria.The complete evaluation method is presented in Appendix I. The overall results are shown in Table 7.2

7.1 Evaluation Results

In general, the smart manufacturing framework was positively received by the participants in terms ofmeaningfulness, goal relevance and cost of implementation. The implementation approach was evaluatedas very understandable. It also provides an adequate overview of issues that are currently relevant inthe manufacturing industry. The frameworks meaningfulness scored high for both interviewees.

The stages were considered to be feasible. Both participants even mentioned that the assessed TO-BEsituation is not something they would like to reach but rather a mere necessity. For both participants,it was possible to see the roadmap to be implemented within a certain time frame. The expected costsand time are not included in the framework as it depends on the activities that are necessary to reachthe desired maturity stage. However, this was not considered to be a pitfall of the framework becausethe solutions showed were deemed reasonable.

The results for goal relevance showed interesting differences between the participants. Company 1 ratedthis criterion as completely satisfied, while company 2 evaluated goal relevance as moderately satisfied.


CHAPTER 7. EVALUATION

For company 1 the to-be situation was very clearly visualized and helped to create a better understandingof the future situation, hence the high score. Company two already had a clear vision of the futuresituation and would rather have seen a clearer link to the relevant business cases, hence the averagescore. Overall both participants mentioned that the goal that was set related to the strategic goalswithin the company. However, a link to managers’ goals and business value could improve the goalrelevance.

Action implications, i.e. operational validity, were less clear. Indeed the SMF remained conceptual innature and thus scored high on the element conceptual. Note that the roadmap including the activitieswas sent to the participant after the evaluation. Nevertheless, the activities described in the roadmapare also still on a high abstraction level. The overall approach is clear, the outcome of the framework canbe put into practice, but it is lacking really specific solutions. In order to determine clear and accurateactivities a very specific set of knowledge is necessary, which was not in the scope of this project.

The smart manufacturing framework was ranked slightly above average on the innovativeness criteria.Especially the approach of the framework was perceived innovative by both of the participants, as itshowed a combination of high-level, process aspect and pragmatic, integrated aspect, functionalities.However, the participants also mentioned that the solutions that are provided are not new solutions andtherefore do not transcend the commonsense solutions. Nevertheless, the solutions that are providedwere considered to give important insights into practical problems that are relevant for the participantsat this moment.

Table 7.2: Evaluation results

Criteria Result Average Score

MeaningfulnessThe framework was considered very relevant andmeaningful for the participants.

Very Satisfied (4)

Goal relevanceThe framework provides insights in the roadtowards the future situation and aligns the currentsituation and the visions.

Very satisfied (4)

Operational ValidityThe framework provides relatively clear activities inthe roadmap, but it is limited due to the conceptualnature, which makes implications not always clear

Moderatelysatisfied (3)

Innovativeness

The framework elements are not innovative and aremarked as common sense solutions, however theframework approach and roadmap due presentinnovative insights.

Moderatelysatisfied (3)

Cost ofimplementation

The framework shows feasible solutions that mustbe met within a certain time frame, but does notpresent the actual costs and time.

Very satisfied (4)

7.2 Conclusions and Recommendations for Atos

Based on the evaluation of relevance a few conclusions can be drawn that lead to recommendations forAtos.

First, the Smart Manufacturing Framework was received positively by the participating companies. Thepractical usefulness of the framework is considered high. The framework is considered very relevant asit visualizes a topic that is an important point on the agenda for both participants. Both companiesextracted a different value from the framework. For the startup, the goal visualization is a great advantageof the framework. Visualizing the goal and determining a path towards that desired situation helps toextend their limited IT landscape. The manufacturer that already has a well-established IT landscapeand smart manufacturing vision found it valuable that the framework puts it in perspective. It visualizesthe situation and shows if you are on the right track to progress the smart manufacturing journey. Lastly,the pragmatic approach and feasibility were received positively. Due to its commonsense solutions, it is


CHAPTER 7. EVALUATION

clear what needs to happen. The interoperability of known systems can be used to cover most activitiesthat need to occur to fulfill smart manufacturing capabilities. Because the participants were alreadyacquainted with the systems, the feasibility of the approach was assured.

The commonsense solutions and visualization of known and acknowledged software systems do lead to alower score on the innovativeness of the framework. The goal of the framework was mostly to show theinteroperability of the systems. Only if the architecture is correct relevant information can be extractedfrom the large amounts of data that constantly moves through the systems. Only when this happensoptimized decision-making and automatization can happen and new technologies become valuable.

The concreteness of the framework was considered low mainly due to the conceptual nature of theframework. The conceptual approach helps to have a more general framework that is applicable tovarious situations, but it is harder to see its implications. Both participants would have rather seenthe direct implication of activities and more concrete activities. Activities can be extracted from thematurity models directly when creating the roadmap, but they were still considered to be on a highabstraction level. Furthermore, the goal relevance and concreteness of the system can be improved, byincluding business cases. Within Atos, projects are already executed that can be integrated with theframework. Connecting these use cases to a certain maturity stage or activity shows an accurate result.More importantly, it provides insight into the business value that an activity can provide and helps todecide whether or not the activity is relevant.

Based on these conclusions three recommendations for Atos are distinguished.

First, when a company is involved in smart manufacturing practices or wants to orientate itself in thisfield, use the Smart Manufacturing Framework as one of the best practices. The framework includesaspects that are relevant for each manufacturer that wants to keep up with new innovations. Theframework visualizes a complex ecosystem in an understandable and pragmatic manner and is wellreceived by the clients.

Secondly, use the framework as a tool to establish new projects. The framework provides a standardizedmanner to identify gaps within the current landscape. This shows directly new opportunities for Atosto step in and show the possibilities to fill in those gaps. Long-term projects can be established oncethe company starts its smart manufacturing journey and follows the designed roadmap. Using theroadmap long-term projects can be established, where organizations must be guided along their smartmanufacturing journey.

Third, make the framework more concrete during the implementation. The Atos employees possess thenecessary knowledge, to directly come up with concrete solutions. The concrete solutions go beyondestablishing that a Manufacturing Execution System is needed, but include types, functionalities, andan implementation approach. As this knowledge is available within the company, it would be good toimplement it within the framework when it is used. Furthermore, include some business cases for eachmaturity stage that show the value and possibilities of a stage in an actual situation.


Chapter 8

Conclusion and Outlook

This chapter concludes the main results and reflects on the research as a whole. In addition, this chapterprovides an overview of the added value for different stakeholders and indicates directions for futureresearch.

8.1 Research Conclusion

During this project, a framework is created and analyzed to support the development of smart manu-facturing. This artifact is the answer to the following research question that was based on the problemstatement:

”How can technologies, capabilities, and activities belonging to smart manufacturing be structured, suchthat it creates new values for the manufacturing industry?”

The literature review provided a number of reference architectures for various initiatives within thefourth industrial revolution. The analysis of the reference architectures verified that currently there isa lack of reference architecture that helps to support the smart manufacturing paradigm. The existingarchitectures for smart manufacturing are not facilitating architectures, show a high abstraction leveland do not include the technology aspect.

This analysis identifies the need for a complete, facilitating reference architecture that supports thedevelopment of smart manufacturing. Therefore, elements from the existing reference architectures areextracted and positioned on the independent reference framework AFIS. The AFIS framework is adaptedto the fourth industrial revolution, based on the RAMI 4.0 framework. Positioning the elements leadsto the design of the conceptual model as was shown in Chapter 4. The conceptual model provides anoverview of the important characteristics of the smart manufacturing paradigm and has an integratedapproach. The mapping on an independent framework proposes a unified model that provides userswith a central value. However, the conceptual model is still not facilitating the development of smartmanufacturing. The conceptual model only provides a complete overview of the elements that arenecessary within an enterprise but does not show any activities how to get to that desired state. Therefore,it can not be used as a tool by Atos to identify the opportunities for their clients.

A new design iteration is executed to change the conceptual model into an operational model. Theconceptual model is extended by maturity stages and aspects of the modernized Truijens Framework.The aspect dimension is made implicit to reduce the four-dimensional space to a three-dimensional spaceshowing the hierarchy layers, lifecycle phases, and maturity stages. The resulting Smart Manufactur-ing Framework shows five maturity stages based on the SIMMI 4.0 maturity model. Each maturitystage is designed as a self-contained model based on the aspect dimensions plus an additional integra-tion view. The result of the operational Smart Manufacturing Framework is a tool, consisting of 20models, that helps to assess the current IT landscape and define the desired state. The framework isnot yet complete as for example the data and organization aspect are missing. Furthermore, importanttechnologies such as additive manufacturing, virtual reality, and artificial intelligence are not explicitlycovered in the framework. These technologies are not key technologies that are necessary to developsmart manufacturing but can provide additional value to enterprises.

The implementation of the operational Smart Manufacturing Framework showed that the tool can be


CHAPTER 8. CONCLUSION AND OUTLOOK

successfully used in practice. For both case-studies the final result, the roadmap could be constructed,based on the maturity models. During the implementation, the framework was validated by using severalmethods to reach the same result. This approach showed that the Smart Manufacturing Framework wascorrect and recognizable for the participants. The two case studies were two extremes, a start-up anda well-established OEM, which provided insights into both sides of the spectrum. This also led to adifferent type of value that was gained from the framework. For the startup, it was especially interestingto see the growth that they still had to go through and it provided new insights on what functionalitiesthey needed for their production environment. On the other side of the spectrum, the participant alreadyhad a clear vision of the activities they had to perform to get from stage 3 to stage 4. Nevertheless, itwas valuable for this participant to have a reminder of the activities that still needed to be performedand a confirmation that they are on the right track to fully establish smart manufacturing capabilities.The limited number of case-studies does make it difficult to say anything about the general applicabilityof the framework. The implementation succeeded twice, so it was not just beginners luck, but it mightbe the case that the approach needs to be changed in other situations. Nevertheless, every manufacturerneeds an IT infrastructure, to support their product development or production phases (Weber et al.,2017). The outline of the framework is designed using standards, such as ISA-95 and product lifecycles,that can be applied to any enterprise within the manufacturing industry. Therefore, the design is notspecific to a single enterprise. Furthermore, the framework covers standard information systems that arewell-known in enterprises and applicable in the manufacturing industry. For example, every manufacturerthat executes both development and production needs at least a PLM, ERP and MES system (Khedheret al., 2011). This makes the elements within the framework applicable to the entire manufacturingindustry. The framework makes it possible to identify, which systems are available and which systemsare still missing in any situation, due to its general elements. Therefore, it fulfills a business needthat currently exists in the manufacturing industry, by showing the steps they have to take in order toapply smart manufacturing. By fulfilling that need the framework can bring value to the manufacturingindustry as a whole. However, due to the limited case-studies generalizability, is not proven and it isnot certain that the framework provides new value. The result of the framework implementation waspositive and also showed new possibilities for Atos. Therefore, it is recommended to Atos to include itin their best practices.

Not only the implementation of the framework gave a successful result, but the practical usefulness wasalso evaluated positively. The relevance of the framework is considered very high as it points out issuesthat the companies are currently coping with. Due to the visualization of well-known elements, theframework is recognizable and it gives a clear overall visualization of what needs to happen. For thisreason, the framework was not considered to be innovative. It does not show elements that are newand therefore does not appear innovative. Nevertheless, the framework as a whole was considered to beinnovative. The approach and architecture was something that the participants had not seen before. Forexample, the positioning of software elements along with lifecycle phases and a manufacturing hierarchywas considered innovative and the visualization of the maturity stages is also seen as a contribution.Furthermore, the high-level functionalities in the process aspects and the pragmatic view in the softwareaspect were considered new and useful.

8.2 Contributions

First, a contribution to literature has been made by designing a facilitating reference architecture forsmart manufacturing. The analysis showed that there was still a gap in the literature to fill and thisresearch has indicated that the Smart Manufacturing Framework does support the development of smartmanufacturing. The design approach of extracting elements from existing references and mapping theseto stages was not unique, but the result is considerably different. The visualization of the maturitystages based on aspects from the information systems domain is a new way to represent the capabilitiesthat are needed for smart manufacturing. Furthermore, the framework contributes to a business needwithin the manufacturing industry. it untangles the complexity and provides a plan on how to reach acertain vision. The Smart Manufacturing Framework provides a pragmatic solution to create structurein a complex environment. It can be used as an internal tool by companies to create a roadmap for theentire organization.

Second, the conceptual model shows a unified view of the various elements positioned using a manufac-turing standard and lifecycle phases. This research defines the relation between the hierarchy levels and



the three lifecycle phases, while often in this field of research the focus is limited to the production phase.The positioning of elements on a well-known hierarchy standard and for each lifecycle phase is new andprovides an accurate view of the systems and functionalities available within the smart manufacturingparadigm.

Lastly, the SMF can also be used by external parties, more specifically consultants, to show possiblesolutions to bridge existing gaps. This contribution provides business value for Atos. The goal of theframework was for Atos to have a structured approach to provide their customers with new opportunitiesand guide their clients throughout their smart manufacturing journey. Firstly, when a customer is stillin the orientation phase the tool can help to identify the desired situation and create a vision, what theenterprise wants to achieve with smart manufacturing capabilities. Once, this is established there laysan opportunity to engage in a long relationship and build the road together. Atos can provide solutionsin the form of systems and techniques that brings the company closer their goal. This is beneficial forAtos because if they can show that they have solutions to fill in the existing gaps, a client is likely toengage in a mutual project. The SMF provides an opportunity for Atos to easily identify opportunitiesand show their own capabilities.

8.3 Reflection

A research project can not always go flawless, during this project some improvement points have beenidentified during the process. At the beginning of the project, a lot of effort was spent on keeping AFISas it was. This meant that everything was kept the same, even the functionalities, and the elementsof the reference architectures needed to be generalized to fit the framework. After experimenting withthis approach at first, the results were interesting but not as desired. The results showed gaps in termsof functionalities but did not include any of the key elements of smart manufacturing. Therefore, theapproach was changed and the AFIS framework is adapted to fit smart manufacturing features. It wouldhave been better if the conclusion was drawn sooner and the current approach was started earlier in theresearch. This could have led to a more complete framework.

Not all aspects of the modernized Truijens framework are included in the Smart Manufacturing Frame-work. The decision to extend the model using a maturity model was made relatively late during theproject. This led to decisions that must be made in order to ensure the feasibility of the project. A morecomplete framework would also include data analytics techniques, concrete data flows and an organiz-ational model. An earlier decision to include the maturity models could have led to a more completeframework.

The number of case-studies can also be considered as a limitation for the generalizability of the imple-mentation and evaluation. There were two companies considered to be fit for the implementation, fromthe perspective of Atos. For a better conclusion of the applicability of the framework for the entiremanufacturing industry, more case-studies need to be performed.

The final result of the Smart Manufacturing Framework is evaluated during the case-studies, but not bythe intended end-users within Atos. Evaluation and discussion moments took place during the designof the framework, so input from Atos’ side is included in the design. This is important to ensure theusefulness of the framework for Atos is guaranteed. However, to support that the framework will beused by Atos, it is better to have a workshop to show the implementation and possible results. Thisworkshop includes an explanation of the Smart Manufacturing Framework and the relating maturitymodels, so they aware of the content. It explains how the SMF can be used at the clients showing theimplementation approach, so the end users know how to apply it. And lastly, it shows concrete results ofthe case studies to emphasize the value and possibilities for Atos. This approach should create awarenessat Atos for the framework and encourage people to start using it.

8.4 Future Research

First, to enhance proof for the generalizability of the Smart Manufacturing Framework it is recommendedto implement the framework in more case studies. It would be beneficial if the framework would also beimplemented by a real manufacturer with a high throughput of products, to have a different perspectivethan the ones in this research.



There are two extensions possible for the Smart Manufacturing Framework. First, the remaining aspects,the data aspect, and organizational aspect can be included in the framework, such that all aspectsfor implementing smart manufacturing are covered and a complete roadmap can be designed. Second,relevant business cases can be included in the smart manufacturing framework, to make it more concrete.Projects that Atos already executed, can be connected to a maturity stage. This makes the outcome andvalue of a certain activity or stage clearer. For example, build a portfolio of template cases that directlyrelate to the implications.


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Appendix A

Design Science Research (DSR) Paradigm

The DSR paradigm consists of the original framework and is later extended by the three research cycles(Hevner, 2007) and a knowledge contribution framework (Gregor & Hevner, 2013). The visualization ofthese three frameworks is shown in Appendix A.1, A.2 and A.3.

Information Systems Research Framework

The Framework for design science research showing the balance between rigor and relevance is shown inA.1

Figure A.1: Information Systems Research Framework (Hevner, March, Park & Ram, 2004)


APPENDIX A. DESIGN SCIENCE RESEARCH (DSR) PARADIGM

Three research cycles

The three research cycles from Hevner (2007) are visualized in A.2.

Figure A.2: Three Research cycles (Hevner, 2007)

The knowledge contribution framework

The knowledge contribution framework from Gregor and Hevner (2013) is shown in A.3

Figure A.3: Knowledge contribution framework (Gregor & Hevner, 2013)


Appendix B

Results from literature review

The visualization of the architectures, discussed in Section 3.1 and a short explanation of the elementsare presented here. Note that these are the results of the literature review previous to this researchproject (Brouns, 2019).

ISA-95

The description of the levels is based on American National Standard (2005), Jiang (2018)

• Level 0 the physical process;

• Level 1contains activities for sensing and actuating the physical process of level 0;

• Level 2 contains activities that monitor and control the physical processes. A possible entity canbe a supervisory control and data acquisition (SCADA) system.

• Level 3 defines workflow activities to support the production process, for example maintainingthe records. A possible entity can be a manufacturing execution system (MES).

• Level 4 runs the enterprise and contains business related activities such as planning and scheduling.A possible entity can be an enterprise resource planning (ERP) system.

Figure B.1: ISA-95 framework (American National Standard, 2005)

5C & 8C Architecture

The description of the 5C elements is based on Lee et al. (2015) and the 8C facets on Jiang (2018).

• Connection extracts data from the physical environment using its sensor network or manufactur-ing systems;

• Conversion retrieving information from the extracted data by executing some small data analysis;

• Cyber is the central information storage, containing data from various machines. Which allows itto create a digital twin of the machines. Using this information autonomous learning and processpredictions can be established;


APPENDIX B. RESULTS FROM LITERATURE REVIEW

Figure B.2: 5C (Lee, Bagheri & Kao, 2015) and 8C (Jiang, 2018) Architecture.

• Cognition uses the acquired knowledge to support the decision making for future process optim-ization. Visualization of data can play an important role to transfer the knowledge;

• Configuration transfers feedback from the cyber space to the physical world such that the systemacts upon the decision that are taken. This level acts as supervisory control to make machinesself-adaptive.

• Coalition integration different parties along the value and production chain to jointly build asupply chain in a flexible way; am

• Customer focuses on the role of a customer along the lifecycle from the design until the after-sale.This is necessary for the emerging mass customization demand of customers;

• Content enhances the traceability and performance of the product across the lifecycle as all thedata of the product is stored and analyzed along the process

RAMI 4.0

The description of the RAMI elements is mainly based on (VDI/VDE, 2015). The hierarchy levels arebased on American National Standard (1995, 2005) and the definitions of the vertical layers from Frysaket al. (2018) are used.

• Hierarchy Levels represents the functionalities within factories and is based on the ISA-95 stand-ard, adding some additional elements to enhance the Industry 4.0 environment.

– Product is the manufactured product itself;

– Field Device is considered to be an intelligent device such as a smart sensor;

– Control Device combination of sensors and actuators that caries out basic control;

– Station can carry out minor activities and is made up of multiple control modules;

– Work Centers can produce multiple items and execute a combination of methods and tech-niques;

– Enterprise is responsible for the planning of multiple factory sites;

– Connected World describes the collaboration with suppliers and customers.

• Life Cycle is based on the standard for life-cycle management. It makes a distinction betweentype and instance.

– Type shows the basic idea of the product and covers the development and maintenance phase;

– Instance describes the concrete assets and consists of the manufacturing, usage and mainten-ance of the object.

• Vertical Layers present various viewpoints based on ICT standards. The layers help to breakdowna complex system into multiple smaller pieces.



– Asset Layer gathers data from material assets;

– Integration Layer gathers data from immaterial assets;

– Communication Layer includes communication protocols and enables communication pro-cesses;

– Information Layer includes the information model and relevant data;

– Functional Layer provides the horizontal integration and formal description of platforms;

– Business Layer regulates the bussiness processes.

Figure B.3: RAMI 4.0 Framework (VDI/VDE, 2015)

Smart Manufacturing Ecosystem

The description of the elements is based on the article by Lu, Frechette and Morris (2016).

• Manufacturing Pyramid allows the vertical integration of machines, factories and systems. Toenhance self-awareness and self-correction among the factory it is important that information flowsthrough the pyramid;

• Product lifecycle starts at the product design and ends with the end-of-life of a product, including6 phases. One of the phases, manufacturing, is represented by the manufacturing pyramid;

Figure B.4: Smart Manufacturing Ecosystem (Lu, Frechette & Morris, 2016)

• Production lifecycle consists of 5 phases beginning with the design until the decommissioningof the production facility. During their lifecycle the production systems are reconstructed to allowfor new products to be produced. This requires a certain amount of flexibility in the design of theproduction systems;

• Business lifecycle enhances the interaction between supplier and customer, through the sourceplan, make, and deliver phases. As customer demands change faster and ask for personalizedproducts it is important that information flows through the lifecycle such that adaptions can bemade quickly.



Figure B.5: Industrial Internet reference Architecture (Lin et al., 2017)

Industrial Internet Reference Architecture

The description of the viewoints are based on the report by Lin et al. (2017)

• Business viewpoint is concerned with the identification of stakeholders and their visions inthe business context. The state of the objectives is identified through mapping on fundamentalcapabilities;

• Usage Viewpoint represents activity flows involving both human and logical users, trying toachieve the key functional capabilities;

• Functional viewpoint shows the functional components and the interaction and relationshipsbetween these components. These can be both within the system as well as in the external envir-onment;

• Implementation viewpoint considers the technologies needed to implement components fromthe functional layer. These elements coordinate activities in the usage viewpoint and support theobjectives of the business viewpoint.

Software-Defined IIoT Architecture

The descriptions result from the article by Wan et al. (2016).

Figure B.6: Software-Defined IIoT Architecture (Wan et al., 2016)

• Physical Layer is composed of various devices such as sensors and various networks. The nodesin these networks can correspond in real-time and information is send to the control layer.

• Control Layer realizes the communication between the physical and application layer. The controllayer sends information to the physical layer to control the physical assets and make adjustmentsbased on their performance. Information about the performance of the equipment is correspondedto the application layer.



• Application Layer can display carious applications that for example show the performance ofthe machines. Information in this layer can be analyzed such that processes on the lower layerscan be improved and performance is optimized.

Internet of things Architecture Reference Framework

The description of the components is based on the report by Bauer et al. (2013).

• IoT Process Management incorporates traditional process management into the IoT world.This included both modelling and execution of processes.

• Service Organization enables the communication between the various facets, via services as itis the primary form of communication in IoT-ARF. This included the orchestration, compositionand choreography of the services.

• Virtual Entity provides interaction with the IoT system based on the virtual entity, which is adigital representation of the physical entity. It manages associations for services and other virtualentities.

• IoT service contains IoT services and enables discovery and look-up of various IoT services. AnIoT service can be used to collect and deliver data via the network

• Communication provides a common interface for the IoT services and provides a platform forthe variety of interaction schemes of the IoT systems.

• Security ensures the security of the IoT platform based on, authorization, trust, key exchange,identity management and authentication.

• Management is responsible for cross-functionality between the various IOT systems. The man-agement facet considers, configuration, fault, reporting, member and state. It connects the variousconcepts, facets and members of the IoT system. Furthermore, it keeps track of the state, as wellas faults and configurations of these aspects.

Figure B.7: IoT-ARF (Bauer, Boussard, Lucent, Bui & Carrez, 2013)


Appendix C

Analysis of frameworks

Table C.1 shows the sub-dimensions and the corresponding criteria. The reference architecture types andtheir corresponding values are shown in Table C.2. lastly, Table C.3 provides the results of the analysisof the frameworks.

Table C.1: Multi dimensional space for reference architectures (Angelov et al., 2012)

Dimension Sub-Dimension Criteria

Context

C1: Where will it be used? Single-Organization

Multi-Organization

C2: Who defines it? Software Organization

User Organization

Independent Organization

C3: When is it defined? Preliminary

Classic

GoalG1: Why is it defined? Standardization

Facilitation

Design

D1: What is described? Components and Connectors

Interfaces

Protocols

Algorithms

Texture

D2: How detailed is it described? Detailed

Semi-detailed

Aggregated

D3: How concrete is it described? Abstract

Semi-concrete

Concrete

D4: How is it represented? Informal

Semi-formal

Formal


APPENDIX C. ANALYSIS OF FRAMEWORKS

Table C.2: Overview of the reference architecture types and their corresponding values

Type 1 Type 2 Type 3 Type 4 Type 5

C1 Multiple Single Multiple Single Multiple

C2 Standardization Software Independent Software Research Center

C3 Classical Classical Classical Classical Preliminary

G1 Standardization Standardization Facilitation Facilitation Facilitation

D1 Components Components Components Components Components

Interfaces Interfaces Interfaces Texture Algorithms

Texture Texture Texture Protocols

D2 Aggregated Aggregated Aggregated Aggregated Aggregated

Detailed Semi-detailed Semi-detailed Semi-detailed Semi-detailed

Detailed Detailed Detailed

D3 Abstract Semi-concrete Abstract Abstract Abstract

Concrete Semi-concrete Semi-concrete

Concrete

D4 Semi-formal Semi-formal Semi-formal Semi-formal Formal

Formal Informal Semi-formal

Table C.3: Overview of the identification of the RA dimension values

Dimension 5C RAMI SME IIRA IIoT IoT-ARF

Context C1 MO MO MO MO MO MO

C2 IO IO IO IO IO IO

C3 Classic Classic Classic Classic Classic Classic

Goal G1 F S S F F F

Design D1 Component Component Component Component Component Component

Texture Interfaces Interfaces Interfaces Interfaces Interfaces

Texture Protocols Protocols Protocols

Texture Texture Texture

D2 Aggregated Aggregated Semi Detailed Aggregated Detailed

D3 Abstract Abstract Semi Semi Abstract Semi

D4 Informal Informal Informal Informal Informal Semi


Appendix D

The Three-dimensional Design Cube

A visualization of the three-dimensional design cube with the different levels as was mentioned in Section4.3 is provided in Figure D.1.

Figure D.1: Three-dimensional design cube, based on Grefen (2016b)

Aggregation dimension

The aggregation dimension determines the level of detail, based on the number of components that areidentified in the design. An example is given in Figure D.2. The levels are defined as follows:

• Level 1 is a single component, also known as blackbox;

• Level 2 sub-systems that show a high-level architecture structure;

• Level 3 components are available within the main architecture structure;

• Level 4 components are decomposed into sub-components.

Figure D.2: Visualization of the aggregation levels


APPENDIX D. THE THREE-DIMENSIONAL DESIGN CUBE

Abstraction dimension

The abstraction dimension determines the concreteness of an architecture. The elements in an architec-ture can be either general or precise. An example of abstraction levels is visualized in Figure D.3. Theabstraction levels are defined as follows:

• Level 1 the class type of the component indicates the functionality, general software system;

• Level 2 system type components specify a certain type of system that supports the level 1 func-tionality;

• Level 3 system components are vendor specific.

Figure D.3: Example of abstraction levels

Realization dimension

The realization dimension defines the orientation of the architecture. This can range from technologyoriented to business oriented (Grefen, 2016b). The BOAT framework is used for the realization levels,shown in Figure D.4.

• Business (B) contains the business goals and answers to why a certain architecture exists. It doesnot consider how things are done;

• Organization (O) describes the structure of organizations. This mainly consists of business pro-cesses and organization structures;

• Architecture (A) this level actually includes information systems and shows the software structureto support the organizations;

• Technology (T) depicts concrete ingredients, such as protocols and software, to describe the tech-nological realization of the architectures specified at the architecture level.

Figure D.4: Example of realization dimension, taken from Grefen (2016a)


Appendix E

Setup semi-structured interview

Note, in case the interviewee was not familiar with the project, a brie introduction regarding the projectwas provided by prepared slides.

The goal of the interview was to get an overview of the current status of Industry 4.0 practices within Atos.Furthermore, a first evaluation of the designed framework was done during this interview. Especiallythe relevance of the framework and the goal for an Industry 4.0 tool, should result from the interviews.Depending of the expertise of the interviewee the answers and results can differ. The following questionswere asked.

Current status of Industry 4.0 practices at Atos

1. Which solutions for Industry 4.0 does Atos currently provide?

2. What are current best practices for Industry 4.0 within Atos?

3. What type of tool would be useful for Atos?

4. What would be the goal of this tool?

Brief framework evaluation

1. Are the elements in the framework recognizable?

2. Is the framework self-evident?

3. Would the framework be useful as a tool for Atos?

4. Which aspects should be adapted to serve Atos better?


Appendix F

Maturity Models

Table F.1: Overview of the Maturity Models

Maturity Model Objective Focus Stages

SM3E (Mittal, Romero & Wuest,2018)

Support Small and Medium sized enterprisesthroughout the digital journey

SME companies 5

Maturity Model Industry 4.0(Schumacher, Erol & Sihn, 2016)

Assessing industrial enterprises in discrete manufac-turing based on 9 organizational dimensions

Multiple organiza-tional dimensions

5

Industry4.0-MM (Gokalp, Sener& Eren, 2017)

Provide a common base for performing an assessmentof the establishment of Industry 4.0 technologies


6

Acatech-Industrie 4.0 MaturityIndex (Schuh, Anderl, Gause-meier, ten Hompel & Wahlster,2017)

Offer enterprises practical guidance for developingand implementing an Industry4.0 strategy


6

SIMMI4.0 (Leyh, Bley, Schaffer& Forstenhausler, 2016)

Classify a companies’ maturity based on the fourmain requirements of Industry 4.0

SMEs, mainly soft-ware landscape

5

Guideline Industrie 4.0 (Anderlet al., 2015)

Support small and medium sized German companiesin identifying potentials in relation to Industrie 4.0

German SMEs 5

IMPUSLS-Industry 4.0 readiness(Lichtblau et al., 2017)

Assess the willingness of companies to implement in-dustry 4.0 ideas

German Mechan-ical EngineeringIndustry

3

Maturity and Readiness modelfor industry 4.0 strategy (Akdil,Ustundag & Cevikcan, 2018)

Create a model that is suitable for planning andtransforming their operations for Industry 4.0 con-sidering a broad organizational perspective

Multiple organiza-tional aspects

3

Smartness assessment frameworkfor smart factories (Lee, Jun,Chang & Park, 2017)

Assess the vision of the future factory incorporatinginformation and communication technology based onthe concept of operation management to fit the man-ufacturing industry

Smart Factory 5

Maturity Model for Data-Driven Manufacturing(M2DDM)(Weber, Konigsberger, Kassner& Mitschang, 2017)

Guide companies in adopting principles of I4.0 aswell as criteria to meet the defined maturity stages

Smart factory 6


Appendix G

Smart Manufacturing Framework

The Smart Manufacturing Framework consists of 5 maturity stages. Each maturity stage consists of 5maturity models that all display a different aspect dimension, process, software and platform aspects andan integrated model of the software and platform aspects showing the interoperability of the systems.As was already mentioned in Chapter 5, at least 225 design decisions are made during the process ofdeveloping the SMF. This appendix shows the 20 maturity models of the SMF and provides a shortdescription of the design decisions that are made, including an argumentation. Not each argument isbased on rigor or relevance, sometimes it was merely a gut feeling. In case it is the later I will try todescribe my reasoning as accurate as possible.

Level 1 : Basic Digitization

The first maturity level represents a company that has not yet addressed Industry 4.0 activities. Digit-ization of the processes is limited or none existing.

Process Aspect

Figure G.1: Process Aspect Basic Digitization

• According to (Leyh, Bley, Schaffer & Forstenhausler, 2016), there is no digital support for productdevelopment. Therefore, product information is scattered across the organization (Batenburg et al.,2006). The lack of digital support also suggests that there are no digital tools for product design.However, based on expert knowledge the is not the case in real-life. Hence, each divisions has itsown development tool.

• The functionalities, plotted on site level and enterprise level, are based on the ISA95 standard


APPENDIX G. SMART MANUFACTURING FRAMEWORK

dimensions (American National Standard, 2005). The functionalities plotted on site level are allconsidered level 3 functionalities, while resource management is considered a level 4 functionality(Erasmus et al., 2018). For this reason, a distinction in positioning the functionalities on thehierarchy levels is made. Level 4 resembles the enteprise level and level 3 operates on level loweron the site level.

• It is assumed that the necessary manufacturing equipment is available on the shop floor andembedded with standard machine software. This enables partially digitized production supporton a lower level. The traditional manufacturing pyramid is established. Therefore, production isplanned on a site level and resources are planned on enterprise level (American National Standard,2005). These systems are not integrated so the planning is shared with shop floor workers, suchthat the processes can be executed. In an agile environment this means that the workers have tomake fast improvements based on the tons of data they receive, which is an almost impossible job(Weber et al., 2017).

• The company is not service oriented and therefore provides only limited support. There is nosignificant relationship with the customer (Park & Kim, 2003).

Software Aspect

Figure G.2: Software Aspect Basic Digitization

• Software tools are available for the design of products, often they are split in three different cat-egories M-CAD for mechanical, E-CAD for electrical and SWD for software development, basedon (Lu, Frechette & Morris, 2016) and expert knowledge within Atos.

• The software systems on site level all take care of one of the functionalities mentioned in the processaspect. As the organization is still siloed, individual systems are implemented.

• Production is supported by a Programmable Logic Controller (PLC) on the work cell level. This is aremote terminal unit, that due to the programming capabilities has established a more sophisticatedmanner of monitoring and control (Endi, Elhalwagy et al., 2010). The PLC is a common practice incurrent industrial systems and provides a real-time operation, based on its connection with sensorsand actuators as well with higher level SCADA systems (Tomlein & Grønbæk, 2017).

• Batch Sequential data flow between SCADA and some of the information systems (Grefen, 2016b).Continuous and anytime data availability can not be ensured (Leyh, Bley, Schaffer & Forstenhausler,2016).



Platform Aspect

Figure G.3: Platform Aspect Basic Digitization

• A fieldbus enables the connection between SCADA and PLC and is a standard element in themanufacturing industry. A field bus is an umbrella term for standards such as MODBUS andTCP/IP (Endi, Elhalwagy et al., 2010). This connection can be either hardwired, point to point,or via radio.

Integrated Aspect

Figure G.4: Integration of software and platform aspect Basic Digitization



Level 2: Cross-Departmental Digitization

Individual departments are now digitally supported. Industry 4.0 activities are present across the en-terprise. However, data exchange is not automated across departments, therefore the activities are stillpartially decentralized.

Process Aspect

• Data is integrated within the departments, this means that all the different systems within the de-partments are integrated. The SIMMI 4.0 model stage descriptor, cross-departmental digitization,suggests full digitization between departments. However, the description of the stage mentionspartial digitization between departments (Leyh, Bley, Schaffer & Forstenhausler, 2016). Basedon discussions with experts it is decided that automated information sharing happens within de-partments, but that the connection to other departments is still offline. This is an importantassumption made, based on the real-life situations these experts experienced.

• No automated data exchange between departments, therefore still partially decentralized

• One step closer to the smart factory so communication between machines and self-aware productionlines are available . Self-awareness is the second level in the 5C-architecture and suggests data con-version of multiple machines to execute basic analysis such as prognostics and health management(Lee et al., 2015).

Figure G.5: Process Aspect Cross-Departmental Digitization

Software Aspect

• Integrated system for manufacturing Manufacturing Operations Management System (MOMS) thatintegrates all the functionalities displayed in stage 1. It is discussed that an elaborate MES systemcan handle all the various functionalities. However, for clarity we visualize it as an independentintegrating system (Erasmus et al., 2018).

• The Business Process Management System (BPMS) helps to orchestrate the activities from thevarious information systems within the MOMS (Erasmus et al., 2018). This ensures the efficientexecution of activities. This approach is based on the architecture of the HORSE project (Grefen,Vanderfeesten & Boultadakis, 2016b). The Horse software supports the actual manufacturingprocesses and thus connects the information systems on site level to the machines and employeeson work cell level.



• The new technology stack of Porter and Heppelmann (2014) shows that software must be embeddedin the things, in this case the machines on a work cell level. This IoT software embedded in themachine, requires sensors and actuators embedded in the machines to generate the data.

• Customer database is established, information can be accessed by file transfer. No analysis aremade based on the information on the customer, which means that there is no added value createdbased on relationship management (Park & Kim, 2003).

• PLM system is established for the development department. It is mainly seen as a product datamanagement tool that can push some of the information to the production department. Productionengineering is executed locally within the MOMS (Batenburg et al., 2006).

• Systems engineering software covers the development of total systems. In other words it laysdown the structure of a product based on customer requirements. As it integrates the varioustypes of components and provides the basis for the development of the entire product, the systemsengineering software is positioned on the production line level. (Albert & Mirko, 2007; CMMIteam,2001)

• The data of an entire product is managed in a product base on site level, as at the end of productionin the factory a certain product is delivered. In most cases a PDM is not necessary when a PLM isavailable as all prodcut-information is stored in the PLM. However, to provide a complete overviewof the possible systems on the various hierarchy levels the PDM is included in the model.

Figure G.6: Software Aspect Cross-departmental digitization



Platform Aspect

• Workcell and production line in the production department are connected by a fieldbus (Endi,Elhalwagy et al., 2010).

• The component data is no longer siloed, but are stored in one central database. All the differentdesign tools can access that database to open the files they need, this is known as a shared re-pository (Grefen, 2016b). A hared data storage also supports the congruence between the variouscomponents.

• The manufacturing activities on site and the business process management system responsible forthe manufacturing processes, are connected via an ESB (Erasmus et al., 2018).

Figure G.7: Platform Aspect Cross-Departmental Digitization

Integrated Aspect

Figure G.8: Integrated software and platform Aspect Cross-Departmental Digitization



Level 3: Vertical and Horizontal Digitization

The smart factory is established and there is complete horizontal and vertical digitization. ConnectionPLM, ERP, MES is necessary to enable the connectivity between departments and the establishment ofthe smart factory (Khedher et al., 2011).

Process Aspect

• Smart Factory is established, this means there is machine to machine communication, productionlines are no longer controlled individually but are connected with each other. The shop floor iscompletely autonomous and connected to the enterprise resource systems. Production is controlledfrom an enterprise level (Wang, Wan, Li & Zhang, 2016).

• Establishment of a digital product twin, this means that a complete virtual representation of aphysical product is available (Tao et al., 2018). Based on sensed data from the production facilitysimulations can be executed to provide a better design of the product. Therefore, product lifecyclemanagement stretches across development and production. Production engineering is now integ-rated with the product development and automatically shared with the production environment(Kiritsis, 2011).

• A service organization is established and services are accessible across the organization. This meansthat services are not only depending on the product development but the availability of servicescan also change product. This requires a certain level of cross departmental service management(Wiesner, Freitag, Westphal & Thoben, 2015).

• The services do not stretch beyond corporate borders therefore product support is still a decent-ralized activity which is not integrated within the functionalities of the enterprise. The customeris considered an identified customer based on customer data management (Park & Kim, 2003).

Figure G.9: Process Aspect Vertical and Horizontal Digitization



Software Aspect

• Services across the enterprise which means that these services must be designed, maintained andprovided. To manage these activities a service lifecycle management(SLM) system can be used.According to Wiesner et al. (2015), it is best to integrate the PLM and SLM, such that theactivities influence each other. However, for clarity reseaens and to emphasize the importance ofservice lifecycle management the systems are displayed individually. These systems still operatewithin enterprise borders

• PLM stretches across development and production. Furthermore it is now integrated with the ERPand MOMS (Khedher et al., 2011).

Figure G.10: Software Aspect vertical and horizontal integration

Platform Aspect

• The smart factory is established using a special Smart factory information Service Bus. Thisenterprise service bus enables the integration of both information systems as well as shopfloorhardware and IoT compontens (Yoon, Um, Suh, Stroud & Yoon, 2019)

• The IoT communication layer serves as the connectivity between the IoT software and the middle-ware on the higher levels (Porter & Heppelmann, 2014). The high level middleware can be usedto integrate the enterprise systems and the IoT communication layer can ensure the connectivitybetween the ERP system and production data.

• Vertical Integration within the development phase is established by an enterprise service bus andconnects on a site level to the production phase. To match product specifications from PDM withproduction specifications in the MOMS. The integration between development and production canalso happen on an enterprise level, using ESB as a middleware between PLM, ERP and MOMS.

• The enterprise systems can be operating an the cloud. As the cloud is not an actual platform itis visualized as an annotation based on the example by (Grefen et al., 2016a). The cloud plays animportant role in the analysis of high volume data (Oztemel & Gursev, 2018). In order to optimizemanufacturing processes and realize a smart factory is is important to extract value from the datagenerated by devices, sensors, machines and information systems (Guoping et al., 2017). Fore,example the IoT data from the shop floor is used to conduct smart analysis for decision support.Furthermore, if data is used across various lifecycles cloud applications often helps to standardizethe data formats that supports data integration.

• According to (Carlsson, Hegedus, Delsing & Varga, 2016), the manufacturing pyramid can betransferred to a local automation cloud architecture. So for example, the MOMS and ERP system



can be in a local cloud and be connected to another local cloud that holds the lower level systems,such as the SCADA and PLC.

Figure G.11: Platform Aspect Vertical and Horizontal Digitization

Integrated Aspect

Figure G.12: Integrated Aspect Vertical and Horizontal Digitization



Maturity Level 4

Companies digitization stretches beyond corporate borders. The role of the customer and the fastinnovation cycle becomes important for improved development and autonomous production.

Process Aspect

• Self-optimizing smart factory based on M2DMM, data is aggregated on the shop floor and basicruntime analytics can be executed locally. Data is aggregated and analyzed on enterprise levelto support decision making, which leads to an optimized manufacturing planning and execution(Weber et al., 2017).

• Smart enterprise is established. Supply Chain management, Customers Relation Management,Resource Planning, Product lifecycle managment, are no longer individually managed systemswhen there is Smart Enterprise Control (Conway, 2016). Data is integrated across the productlifecycle which leads to optimized decision making for example in the product phase based on theaccurate information that is available.

• The customer is integrated and the relationship between the company and the customer is estab-lished (Park & Kim, 2003). Value analysis help to provide better services to the customer. , thisis also known as a core customer

• Services are no longer internal but are accessible across company borders. This leads to remoteproduct monitoring and supporting the employees during their maintenance tasks using AR.

• Value network control is established once the information of the smart enterprise is accessible forthe value chain partners. Enterprise information systems accessible for value chain partners (Leyh,Bley, Schaffer & Forstenhausler, 2016).

• Supply Chain management can be optimized due to real-time tracking and controlling of productsthroughout the supply chain (Tjahjono, Esplugues, Ares & Pelaez, 2017). This information leadsto optimized enterprise planning

Figure G.13: Process Aspect Full Digitization



Software Aspect

• The collaborative value network is established therefore a supply chain management tool is used toaggregate product information across the supply chain. The role of the SCM is mainly to integratethe information from local information systems of each partner in the value network and make itaccessible (Tjahjono et al., 2017).

• Customer service is now an integrated functionality of the enterprise and therefore customer dataand relationship value is managed throughout the CRM system (Park & Kim, 2003). It is ques-tionable if a separate CRM system is necessary or this information can also be included in thePLM and SLM system. Nevertheless, good service to the customer is of create importance for theperformance of the organization, so a dedicated system might be worthwhile (Yerpude & Singhal,2018).

• IoT software is added to enable the smart product such that data can be generated from the productin use and its ecosystem. It is important that the services that are required for the smart productas well as the product development are integrated such that the most value can be gathered fromthe product in use, statement from an expert within Atos.

Figure G.14: Software Aspect Full Digitization

Platform Aspect

• The ESB is now extended beyond corporate borders across the enterprise. The enterprise systemscan be in the cloud, which makes services and applications of the information systems available forsuppliers and customers. Again the standardization of the data information systems when usinga similar application in the cloud, would allow for improved data integration. The cloud wouldsupport real-time tracking of the product across the supply chain.

• The IoT communication layer now also stretches towards the usage/service phase such that thereis a connection between the IoT software and the higher level middleware to process and store thegathered data from the product.



Figure G.15: Platform Aspect Full Digitization

Integrated Aspect

Figure G.16: Integrated software and platform aspect Full Digitization



Maturity Level 5

Optimization of the already established components. Ideal result of the smart manufacturing paradigm.Based on the mapping of the α-model in section 4.3.3.

Main difference is the optimized analytics, based on real time data. Only the process aspect changes theother aspects stay the same as the information systems that are used are similar. The way the data isprocessed and value is extracted differs and moves towards self-learning as is suggested in the highestlevel of the M2DMM maturit model (Weber et al., 2017).

Process Aspect

• The value network is optimized due to strong collaborations with partners a single source of truthfor product information and new business models and end-to-end solutions.

• All phases of the product lifecycle are connected and optimized based on virtual simulation. realtime analysis and accessible product information across the lifecycle.

• The development phase is optimized as design data, production data and customer data are allused to improve the design of the product based on smart analytics and simulations

• The smart factory can organize itself based on information from the value network, supply chainpartners and customers as well as information from the development phase

• The consumer is connected via personalized accessible services as well as smart connected productsthat generate information based on the environment and can be controlled remotely.

Figure G.17: Process Aspect Optimized Full Digitization



Software Aspect

Figure G.18: Software Aspect Optimized Full Digitization

Platform Aspect

Figure G.19: Platform Aspect Optimized Full Digitization



Integrated Aspect

Figure G.20: Integrated Software and Platform Aspect Optimized Full Digitization


Appendix H

Implementation

The elements that are used for the implementation approach are presented in the first section of theappendix. Therafter, the elaborated results from the gap analysis and roadmap are discussed. A detailedoverview of the implementation approach is presented in the first section of this appendix. Thereafter,the results of the implementation are discussed.

Implementation approach

Detailed questionnaire

The second method is a detailed questionnaire based on the four aspects of smart manufacturing men-tioned in Section 4.2. The approach of the interview is semi-structured. The questions that serve as aguideline for the interview are shown below. The semi-structured approach leaves some flexibility duringthe interview, to ignore questions that are irrelevant and ask follow-up questions where necessary.Basedon the answers the current maturity level of the comoany can be determined.

Vertical Integration

Which Industry 4.0 activities are currently performed?

How is your company organized?

• Is there a department for product development?

• How many production facilities are there?

On which hierarchy level does production planning occur?

• Is there a flexible planning across the various production facilities?

Which systems are used to support the product development?

How is information shared within the departments?

• Is information siloed?

• Is there a common database?

Do you use information systems to support business planning?

What is the level of automation within the factory?


APPENDIX H. IMPLEMENTATION

Horizontal Integration

Which areas of your company are supported by information systems?

Is there integration between information systems?

Is information automatically transferred among departments within the company?

How is data shared with supply chain partners?

• Is there one connecting source via a platform?

• Is data always available for partners?

Is there automatic production control based the resource planning or customer orders?

End-to-end Integration

Can products be tracked across the lifecycle?

How is information from the product during production used to develop the product?

How is information from the product during the usage period used to develop the product?

How is the support of the product arranged during the usage period?

Are products designed and build as smart products? Which functionalities do they have?

Technology support

Which platforms are currently supporting the information systems and applications?

Are there cloud based solutions?

Which Internet of Things applications are used?

Are there services available within the company?

Are these services accessible enterprise-wide?

Which big data applications are used?

Ranking of smart manufacturing key capabilities

Table H.1: Smart manufacturing functionalities and supported key capabilities, taken from Lu, Frechetteand Morris (2016).

Functionality/System Key Capability support

Product Lifecycle Management Quality, Agility, Sustainability

Supply Chain Management Agility, Quality, Productivity

Design for Supply Chain Management Quality and Agility

Continuous process improvement Quality, Sustainability, Productivity

Continuous Commissioning Productivity, Agility, Sustainability and Quality

Design for Manufacturing and Assembly Productivity and Agility

Flexible Manufacturing System Agility

Manufacturing Pyramid Quality, Agility, Productivity and Sustainability



Implementation Results

The detailed results of the implementation for both the gap analysis and the roadmap are given here.

Case-study 1

Gap Analysis

First the gap between maturity stage 1 and 2 must be bridged. This is only a software system implement-ation, namely the MOMS. The BPMS is to automate the manufacturing activities, which is in this caseonly relevant for automatic messages to the shop floor employees and not for machines. Furthermore, theinterviewee mentioned that the BPMS should stretch across the entire enterprise such that all processesare defined accurately.

The gap between 2 and 3 exists for both the platform and software aspect and thus implicitly also on theprocess aspect. Platforms to support both the vertical integration of the development and productionphase, i.e. ESB, SIBUS and IoT Communication Layer, must be implemented. To support the horizontalintegration the ESB on enterprise level is established. This leads to integration of the ERP, PLM, SLMand MOMS. The SLM system is a new system that must be implemented to ensure that the organizationis service-dominant. Furthermore, both the PLM and SLM now stretch beyond their department andprovide more end-to-end integration. Data analysis should be improved to establish the digital producttwin and smart factory.

The gap towards level 4 applies again to all the aspects. The company is now engaged in a value networkand thus two new systems are necessary, the SCM and CRM to connect to suppliers and customers. TheSCM, CRM, PLM and SLM should all be available on a network level and might be in the cloud. Theshould be integrated by an ESB that also stretches towards the ERP system.

Roadmap

Activity 1

Make a choice for a information system that support the production facility. Instead of first implementingindividual systems for the various functionalities, as is suggested for stage 1, it is recommendable todirectly implement a MOMS. The MOMS covers all the functionalities, Quality, Maintenance, Operationsmanagements. Note Inventory management is already covered by the ERP system. When implementingthe MOMS directly the first stage is omitted and the step towards the second maturity stage is madedirectly.

MES systems of various vendors are available that provide the above mentioned functionalities. For thisreason a MES system including these functionalities would be sufficient to fulfill the requirements ofstage 2.

Activity 2

Directly after the implementation of the MES follows the second step. It is important to integratedevelopment and production. This is possible by generating the work instruction in the PLM system,based on the product data that is available, and provide this to the MOMS system. The functions andpossibilities of the production then match directly with the design of the product. The PLM system isintegrated with the MOMS and ERP system via an ESB. This fulfills a level 3 requirement

Activity 3

At stage 3 the enterprise should be service-oriented. Therefore, a SLM should be implemented in theenterprise. The company must determine which services are provided within the organization and makesure that they are accessible enterprise-wide. The management and improvement of services can bebased on product information from the PLM system. This makes it a follow-up activity from productdevelopment. It is better to integrate the SLM and PLM. Once changes occur in the process of productdevelopment or production, the direct integration of the two systems allows for an immediate improve-ment of the services. The processes should also be modelled in such a way that they are service-oriented.

Activity 4

Improved data analytics to establish the product twin and smart factory. The results from the data



analysis can improve the quality and flexibility within the enterprise. Using cloud applications canaccelerate the use of data analytics as standard data formats are used and many cloud applications havestandard data analytics functionalities.

Activity 5

The first step towards stage 4 maturity is the accessibility of the information in the PLM system towardsthe partners in the value chain. The PLM system must be transferred to a network level and preferablybe a cloud application, such that it can connect with other systems from for example suppliers. It isimportant to ensure a high IT security such that data cannot be changed without permission. Theintegrated SLM system moves together with the PLM system such that the services are also availableacross enterprise borders.

Activity 6

SCM system implementation leads to integration between your own systems and the various systems ofthe suppliers. The SCM should at least be connected to the ERP and PLM system to ensure that theinformation is up to data and thus correct. Due to the frequent updated data from within and outsidethe enterprise, scheduling of production and logistics can be done more accurate.

Activity 7

Connecting the customer using an integrated CRM system with a friendly user interface. This makes iseasier to fulfill the customers demands. A sales configurator can be part of this customer system. Asthe system is integrated with the enterprise systems PLM and ERP, the requests of the customer canbe verified based on information that is available in those systems. For example an accurate productionplanning can be made, based on the available resources and product information, such that the customerknows exactly when the product arrives.

Case-study 2

Gap Analysis

The main gap that must be bridged is the one between maturity stage 3 and stage 4. Although, some ofthe elements are still missing to fully support stage 3 maturity, namely the dedicated SLM. Once thatis established the company can move from stage 3 to 4. The gap between these stages applies on theplatform, software and process aspect. The company should engage in a collaborative value network andthus an integrated CRM system must be implemented. Furthermore, multiple systems must be madeaccessible on a network level, such as the PLM, SLM and SCM. These systems must all be integrated byan ESB and could benefit when they are in the cloud, because of standardization. Another importantgap is the development of smart products. The products contain IoT software, such that the data in usecan be processed in real-time.

Roadmap

Activity 1

Implementation of digital service lifecycle management. To fully establish level 3 maturity it is necessaryto provide services that are available within the enterprise. These services can for example hold, activitybased production process steps or augmented based production. Therefore, it must be determined whichservice are provided within the organization. Managing and improving the services can follow from theproduct information in the PLM system and would therefore be a follow-up of the product development.A better approach is to integrate the Service Lifecycle Management and the PLM system. As soon aschanges occur in the production process or product development, this can lead to changes in services.The SLM and PLM should be integrated and available via cloud, so it is accessible within the entireenterprise.

Activity 2

The information systems should be cloud-based applications, which makes data analysis easier and moreimportant to ensure quality and flexibility. It is important to perform data analytics for both productdevelopment and production systems. These analysis can be simulations, based on product informationto simulate its performance, or analysis based on deviation measured in the MOMS system. This canlead to improvements in product design or the way of production. Value is created based on the digital



product and production twin, ensuring all data is available digitally to perform relevant analysis.

Activity 3

The PLM system and ERP system should be available on a global, enterprise-wide level, and be stand-ardized. These systems should be integrated to the local available enterprise systems, such as PDM,MES, Warehouse Management and CRM systems. Data migration is of great importance here, as thelocal systems might differ and therefore the data structure and information might differ. Establishinga standard base in the PLM system and ERP can secure quality and productivity around the globe, asthe same, usable information is available to every site.

Activity 4

The first step towards a level 4 maturity is the IoT program for products in use. As a start is alreadymade in this direction it makes sense to pursue this even further. It is important that the services that areprovided based on the new opportunities are also adapted in the SLM. Furthermore, the implementationof IoT for products at the clients, also changes the development of the product. It is important thatduring the product design and development the IoT requirements are integrated. The data that isgathered from the product in use, serves as an important source for data analysis to establish predictivemaintenance, but also improvements of the product.

Activity 5

Secondly the accessibility of the PLM system for the partners in the value chain. The PLM systemshould therefore be available on a network level and accessible via cloud. To share the data automaticallybetween suppliers, bi-directional, the IT security is of high Importance, such that information cannot bechanged in the systems unauthorized. The integrated SLM system also moves to a network level, suchthat the services are available across the value chain.

Activity 6

Implementing a global supply chain management system. This system should be integrated with thePLM and ERP system, such that data is up to date and the correct information is available for supplychain partners. The SCM acts as an integration method between the various systems of suppliers andmostly uses data from the ERP system. Based on the frequent updates of data and also RFID availabilityfor product tracking, data is more accurate, which improves production planning and product delivery.

Activity 7

Additional link to the customer to establish a fully connected customer. The integrated CRM system,with a user friendly interface, makes it easier to fulfill the customers desires. Sales configurators canbe part of the customer system and integrated with the enterprise systems to determine the feasibilityand accurate processing times based on real-time data. Furthermore, the order can be processed auto-matically, as product information is available and inventory is up to date the orders can be placed atsuppliers using the SCM system. The automation also ensures more agility and quality, without harmingthe productivity and leading to longer lead times.

This high-level roadmap is prepared based on the models shown in this report. The gap that existsbetween the two stages must be bridged. Concrete systems that fit the situation best are not part of thisanalysis, as vendor specific knowledge is not available. Therefore, it remains at high-level conceptualsteps and activities. The gap is split into smaller pieces to make the approach more applicable andfeasible. The approach can be completed by providing structures and recommendations for vendor-specific systems, data-migration, data analytic methods and system integration.


Appendix I

Evaluation

The smart manufacturing framework is evaluated on relevance using the five criteria defined by (Shrivast-ava, 1987). The detailed evaluation approach is provided below.

Meaningfulness

• How does this framework contribute to your understanding of smart manufacturing?

• Is the framework enhancing issues that are relevant?

• Which of the aspects of the framework are most relevant?

• Is the framework approach understandable?

Meaningfulness 1 O O O O O 5

Understandability 1 O O O O O 5

Relevance 1 O O O O O 5

Clarity 1 O O O O O 5

Goal relevance

• Does the framework provide insights how the current situation and future visions are aligned?

• Does the framework help to guide towards future visions?

• Are the performance metrics aligned with possible business goals for your company?

Goal relevance 1 O O O O O 5

Future situation 1 O O O O O 5

Goal definition 1 O O O O O 5

Operational Validity

• Is it possible to put the results of the framework into practice?

• Are the activities concrete?

• Are the implications of the activities clear?


APPENDIX I. EVALUATION

Concreteness 1 O O O O O 5

Clarity 1 O O O O O 5

Operational 1 O O O O O 5

Conceptual 1 O O O O O 5

Innovativeness

• Does the framework address new solutions?

• Are new insights put into practical problems?

• Is the approach of the framework innovative?

Commonsense solutions 1 O O O O O 5

Innovativeness 1 O O O O O 5

Solution Relevance 1 O O O O O 5

Cost of Implementation

• Are the activities feasible?

• Is it possible to reach the next maturity level within a certain time frame?

Feasibility 1 O O O O O 5


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Eindhoven University of Technology MASTER On the road ... · Preface Eindhoven, 16th of May 2019 This thesis is the result of a six-month graduation project for the Master Operations